Supporting Material Vol 1 - Colourful Language

Colourful 

Language 

MAJOR PROJECT 

SUPPORTING MATERIAL 

VOLUME 1 

Eleanor Maclure

Colourful 

Language

LONDON COLLEGE OF COMMUNCATION 

MA GRAPHIC DESIGN 

PART TIME 

PERSONAL TUTOR: JOHN BATESON

MAJOR PROJECT 

SUPPORTING MATERIAL 

VOLUME 1 

Eleanor Maclure

Initial 

Research

Initial Inspiration 

Newspaper Article about Politically Incorrect Colour Terms 

Reference: SMITH, H., 2010. Race row over ‘nude’ White House Dress. The Metro, 21 May. pp.32.

Nude: is the Hot Fashion Colour Racist? by Paula Cocozza 

Michelle Obama must be used to causing a stir with 

her frocks. But she could not have known when she 

chose a floorlength gown in a lovely shade of – well, 

let’s just pass over that for the moment – to meet the 

Indian prime minister last November, the furore that 

would follow. The dress was described by its designer 

Naeem Khan as a “sterling-silver sequin, abstract 

floral, nude strapless gown”. Associated Press said it 

was “flesh-coloured”, the colour of Obama’s own flesh 

notwithstanding. Now AP appears to have revised that 

description to “champagne”, an act that has triggered 

debate about fashion’s use of the word “nude”. “Nude? 

For whom?” asks Jezebel magazine. 

To anyone who reads fashion magazines these terms will 

be familiar. “Nude” shades are everywhere this season, 

having dominated the spring/summer 2010 catwalks, 

from off-white through pale rose to gold (“nude” in 

fashion terms does not refer to anything more exciting 

than these rather muted colours, not even with “nude 

bras”). InStyle magazine goes as far as to say that nude 

is the new black: just about the surest way to exclude 

black-skinned women from adopting the trend, since it’s 

apparently not acceptable to wear black as black, nor 

black as nude. 

Over at Elle magazine, where the May issue sees the 

word “nude” repeated nine times on a single page, 

“nude is the colour for spring/summer”. Editor Lorraine 

Candy says there is nothing wrong with this. “Nude is a 

defined colour. It’s white nude, not black nude, but it’s 

not the colour of my skin either. I’m see-through white. 

With someone as powerful and amazing as Michelle 

Obama, I think it’s the wrong thing to get worked up 

about.” 

The problem is that the language of fashion has form 

in this regard. Beading, fringing and animal prints are 

routinely offered as evidence of a “tribal” trend (though 

that is a word Candy says she crosses out whenever 

she sees it). Last October, a month before Obama 

stepped out in her dress, model Lara Stone appeared 

MAJOR PROJECT SUPPORTING MATERIAL 

blacked up in French Vogue. Black models, meanwhile, 

are few and far between on catwalks and covers. And 

even when fashion editors find synonyms for “nude” 

they are conventionally honey, rose, blush, ivory, words 

commonly used to make an English rose complexion 

seem aspirational. There is nothing new in all this, 

of course: remember American Tan tights, and their 

promise to bring a healthy glow to all those “American” 

(read pale) legs? 

“For me, nude would be if I wore brown,” says Dodai 

Stewart, deputy editor of Jezebel. “I do think that it 

really is exclusionary not to realise that this is not nude 

for everyone.” 

But it isn’t just the description of a colour that is 

potentially offensive here, it’s also the way the look is 

styled, the conception of the entire trend. On the cover 

of May’s InStyle, actor Gemma Arterton appears in a 

frock so close to her skin tone that it seems to seep 

into her chest and shoulders, the two adjacent pallors 

of flesh and dress somehow bleaching each other out, 

lightening further the overall look. On the catwalks in 

Paris, Milan, London and New York, these pale shades 

were presented almost uniformly on pale skins. It’s a 

look that’s all about white skin. 

“Obama looks amazing,” says Reina Lewis, professor of 

cultural studies at the London College of Fashion. “It’s 

a fabulous dress. But on her skin ‘nude’ is revealed as a 

colour rather than neutral.” 

Indeed it seems misplaced to think of these shades as 

neutral, when the debate makes clear that this trend is 

anything but. Pantone, the world-renowned authority 

on colour, may have a “nude” shade, thereby conferring 

a certain official acceptability on all those magazines’ 

usage of the term. But then another N-word was once 

commonly used in clothes catalogues to describe a 

chocolatey shade of brown. (Yes, THAT N-word.) Will 

“nude” one day strike us as equally horrifying? 

Reference: COCOZZA, P., 2010. Nude: is the hot fashion colour racist? The Guardian, [online] 20 May. Available at: [Accessed 15/07/11].

Initial Inspiration 

Descriptions of Colours in Fashion Journalism 

Reference: BLISSETT, B. 2010. Greige is the new nude, girls. The Metro, p. & date unknown.

Blog Post about Colour Disagreements 


Reference: Sheri, 2010. Colour: Is it a cultural thing?. Things and Stuff blog, [blog] 22 October Available at: 

[Accessed 22/10/10].

Other Inspiration 

Spring Snow – A Translation Alison Turnbull, Designed by James Goggin

Reference: TURNBULL, A., 2002. Spring snow – a translation. London : Bookworks. 

MAJOR PROJECT SUPPORTING MATERIAL


The Family Beds by Alison Turnbull, Designed by James Goggin

Reference: TURNBULL, A., 2005. The Family Beds. Oxford : Ruskin School of Drawing & Fine Art. 



The Family Beds by Alison Turnbull, Designed by James Goggin

Reference: TURNBULL, A., 2005. The Family Beds. Oxford : Ruskin School of Drawing & Fine Art. 


Objectivist Philosophy 

A is A: Aristotle’s Law of Identity 

Everything that exists has a specific nature. Each 

entity exists as something in particular and it has 

characteristics that are a part of what it is. “This leaf is 

red, solid, dry, rough, and flammable.” “This book is 

white, and has 312 pages.” “This coin is round, dense, 

smooth, and has a picture on it.” In all three of these 

cases we are referring to an entity with a specific 

identity; the particular type of identity, or the trait 

discussed, is not important. Their identities include all of 

their features, not just those mentioned. 

Identity is the concept that refers to this aspect of 

existence; the aspect of existing as something in 

particular, with specific characteristics. An entity without 

an identity cannot exist because it would be nothing. 

To exist is to exist as something, and that means to exist 

with a particular identity. 

To have an identity means to have a single identity; an 

object cannot have two identities. A tree cannot be a 

telephone, and a dog cannot be a cat. Each entity exists 

as something specific, its identity is particular, and it 

cannot exist as something else. An entity can have more 

than one characteristic, but any characteristic it has is 

a part of its identity. A car can be both blue and red, 

but not at the same time or not in the same respect. 

Whatever portion is blue cannot be red at the same 

time, in the same way. Half the car can be red, and the 

other half blue. But the whole car can’t be both red and 

blue. These two traits, blue and red, each have single, 

particular identities. 

The concept of identity is important because it makes 

explicit that reality has a definite nature. Since reality has 

an identity, it is knowable. Since it exists in a particular 

way, it has no contradictions. 

A might be A but with colours, your A might be different 

to my A. I might see a coat as being yellow, you might 

see it as being bright green. Our perceptions of colour 

are subjective & our ability to articulate them accurately 

with language is limited and fraught with difficultly and 

ambiguity. 

J.S. Mill formulates the law as: “Whatever is true in one 

form of words, is true in every other form of words, 

which conveys the same meaning” (Exam. of Hamilton, 

p. 409). 

Reference: Importance of Philosophy, 2010. A is A: Aristotle’s Law of Identity. [online] Available at: 


Quotes from the book Atlas Shrugged by Ayn Rand 

p. 1016 

‘…the formula defining the concept of existence and the 

role of all knowledge: A is A. A thing is itself.’ 

“Whatever you choose to consider, be it an object, 

an attribute or an action, the law of identity remains 

the same. A leaf cannot be a stone at the same time, 

it cannot be all read and all green at the same time, it 

cannot freeze and burn at the same time. A is A. Or, if 

you wish it stated in simpler language: You cannot have 

your cake and eat it, too.” 

“Man cannot survive except by gaining knowledge, 

and reason is his only means to gain it. Reason is the 

faculty that perceives, identifies and integrates the 

material provided by his senses. The task of his senses 

is to give him the evidence of existence, but the task of 

identifying it belongs to his reason, his senses tell him 

only that something is, but what it is must be learned by 

his mind.” 

“All thinking is a process of identification and 

integration. Man perceives a blob of colour; by 

integrating the evidence of his sight and his touch, he 

learns to identify it as a solid object; he learns to identify 

the object as a table; he learns that the table is made of 

wood.” 

Reference: RAND, A., 1957. Atlas shrugged. Penguin Books: London. pp.1016 


Objectivist Philosophy 

The Law of Identity 

In logic, the law of identity states that an object is the 

same as itself: A : A. Any reflexive relation upholds the 

law of identity. When discussing equality, the fact that 

“A is A” is a tautology. 

“That everything is necessarily the same with itself 

and different from another” is the self-evident first 

principle of linguistics, for it governs the designation 

or ‘identification’ of individual concepts within any 

symbolic language, so as to avoid ambiguity in the 

communicating of concepts between the users of that 

language. Such a principle is necessary because a 

‘symbolic designator’ (name, word, sign, etc.) has no 

inherent meaning of its own, but derives its meaning 

from a cognizant agent who correlates the given 

designator with a conventionally prescribed concept 

that has been previously learned and stored in their 

memory. To put it another way, the principle (law) of 

identity states that although it is permissible to call 

the same concept by many different names, words, 

signs, etc., a fact that makes it possible for there to be 

different languages, it is not permissible, within any 

single linguistic group, to call different concepts by the 

same designator, else the users of the language will not 

know which of the possible concepts they are intended 

to call to mind when they encounter that designator. 

The exception, that proves the rule, is where we are able 

to readily discern which of the different concepts we 

are intended to call to mind by the context in which the 

designator is used, for example, in the case where the 

same word denotes both a noun (a bear) and a verb (to 

bear). 

History 

Parmenides the Eleatic (circa BCE. 490) formulated the 

principle Being is (eon emmenai) as the foundation of his 

philosophy. Aristotle identifies the principle in Book VII 

of the Metaphysics: 

Now “why a thing is itself” is a meaningless inquiry 

(for—to give meaning to the question ‘why’—the fact 

or the existence of the thing must already be evident— 

e.g., that the moon is eclipsed—but the fact that a 

thing is itself is the single reason and the single cause 

to be given in answer to all such questions as why the 

man is man, or the musician musical, unless one were to 

answer, ‘because each thing is inseparable from itself, 

and its being one just meant this.’ This, however, is 

common to all things and is a short and easy way with 

the question.) 

—Metaphysics, Book VII, Part 17 

Both Thomas Aquinas (Met. IV., lect. 6) and Duns Scotus 

(Quaest. sup. Met. IV., Q. 3) follow Aristotle. Antonius 

Andreas, the Spanish disciple of Scotus (d. 1320) argues 

that the first place should belong to the principle ‘Every 

Being is a Being’ (Omne Ens est Ens, Qq. in Met. IV., 

Q. 4), but the late scholastic writer Francisco Suarez 

(Disp. Met. III., § 3) disagreed, also preferring to follow 

Aristotle. 

Leibniz claimed that the principle of Identity, which 

he expresses as ‘Everything is what it is,’ is the first 

primitive truth of reason which is affirmative, and the 

Principle of contradiction, is the first negative truth 

(Nouv. Ess. IV., 2, § i), arguing that “the statement that a 

thing is what it is, is prior to the statement that it is not 

another thing” (Nouv. Ess. IV.. 7, § 9). Wilhelm Wundt 

credits Gottfried Leibniz with the symbolic formulation, 

“A is A. 

Locke (Essay Concerning Human Understanding IV. vii. 

iv. (“Of Maxims”) says: 

... whenever the mind with attention considers any 

proposition, so as to perceive the two ideas signified by 

the terms, and affirmed or denied one of the other to be 

the same or different; it is presently and infallibly certain 

of the truth of such a proposition; and this equally 

whether these propositions be in terms standing for 

more general ideas, or such as are less so: e.g. whether 

the general idea of Being be affirmed of itself, as in this 

proposition, “whatsoever is, is”; or a more particular 

idea be affirmed of itself, as “a man is a man”; or, 

“whatsoever is white is white” ...

J.S. Mill formulates the law as: “Whatever is true in one 

form of words, is true in every other form of words, 

which conveys the same meaning” (Exam. of Hamilton, 

p. 409). 

African Spir proclaims the principle of identity (or law 

of identity A : A) as the fundamental law of knowledge, 

which is opposed to the changing appearance of the 

empirical. 

Reference: Wikipedia, 2010. Law of Identity. [online] Available at: [Accessed 12/09/10]. 


Initial Research 

Images taken at a talk given by David Batchelor at Byam Shaw School of Art in March 2011


Big Rock Candy Fountain by David Batchelor, a Site Specific Temporary Installation above 

Archway Tube Station, November 2010 – March 2011

Initial Research 

A Variety of Examples of Work by David Batchelor


Reference: David Batchelor Online Portfolio, nd. 3D Works. [online] Available at: [Accessed 19/09/10].

Other Research 

Work by Artists Rob and Nick Carter 

Reference: Rob and Nick Carter, nd. Projects. [online] Available at: < http://www.robandnick.com/> [Accessed 05/07/11].

Work by Rob and Nick Carter about Colour Naming and Pantone 

Reference: Rob and Nick Carter, nd. Projects. [online] Available at: < http://www.robandnick.com/> [Accessed 05/07/11]. 



Colour Map by Artists Rob and Nick Carter



Colour Map by Artists Rob and Nick Carter


Visit to the Colour Experience 

Colour Illusions, The Mach Band Effect and Simultaneous Contract

Demonstrations of Refraction, Colour Models and Additive Colour Mixing 



Demonstration of Metamerism

The Munsell Colour System 



Communicating Colour, produced by the SDC

Reference: Society of Dyers and Colourists, 2008. Communicating Colour. [booklet] Bradford : The Society of Dyers and Colourists. 


Key Research 

Do You See What I See? by Beau Lotto 

Roses are red, violets are blue - or are they? The colours 

you see may not always be the same as the colours 

someone else sees… as we see colour through our 

brains, not our eyes. Neuroscientist Beau Lotto explains. 

Colour is one of our simplest sensations… even jellyfish 

detect light and they do not have a brain. And yet 

to explain lightness, and colour more generally, is to 

explain how and why we see what we do. 

The first thing to remember is that colour does not 

actually exist… at least not in any literal sense. Apples 

and fire engines are not red, the sky and sea are not 

blue, and no person is objectively “black” or “white”. 

What exists is light. Light is real. 

You can measure it, hold it and count it (well … sort-of). 

But colour is not light. Colour is wholly manufactured by 

your brain. 

How do we know this? Because one light can take on 

any colour… in our mind. 

Here’s another example. If you look at the cubes to the 

right, notice the four grey tiles on the top surface of 

the left cube and the seven grey tiles on the equivalent 

surface of the right cube. 

Once you’ve convinced yourself that these tiles are all 

physically the same colour (because they are), look at 

the next image down. 

What’s amazing is that now the grey tiles on the left 

look blue, whereas the same grey tiles on the right look 

yellow. The yellow and blue tiles of the two cubes share 

the same light, and yet look very different. 

Colour Memories 

Colour is arguably our best creation, one that is created 

according to our past experiences. 

his is why you see optical illusions, because when 

looking at an image that is consistent with your past 

experience of “real life”, your brain behaves as if the 

objects in the current images are also real in the same 

way. 

If we are using past experience to make sense of light, 

how quickly can we learn to see light differently? It is a 

matter of seconds. To demonstrate this we had a large 

group of people for Horizon try an illusion. 

First notice that the two desert scenes have exactly the 

same colour composition. The skies are both blueish 

and the deserts are both yellowish. 

However, when you stare at the dot between the red 

and green squares for 60 seconds, and then look back 

at the dot between the two desert scenes, the colours 

of the two identical scenes will astound you. 

The more focused you are in staring at the dot between 

the green and red squares the better the subsequent 

illusion will be. 

The desert scenes change colour because your brain 

incorporated its recent history of redness on the left and 

greenness on the right in the second image and applied 

it to the desert scenes when you looked at them for the 

second time … at least for a while. 

These two facts raise an intriguing possibility. Maybe 

colour is more fundamental to our sense of self than we 

thought previously. And indeed it is. 

Remember, colour has been at the heart of evolution for 

millions of years. 

Think of the relationship between insects and flowers 

(flowers are not coloured for our benefit, but for theirs), 

or of all the different colours of animals and how they 

either blend into their environment or, like the peacock, 

stand out in order to attract attention.

Think about the colours of the clothes you are 

wearing… and why you are wearing them. The whole 

of the fashion, cosmetic and the design industries are 

predicated on colour. 

Perception-based Evolution 

What this means is that our simplest perception has 

shaped who we are. What’s more, and this is amazing 

indeed, colour, which remember does not exist, has 

shaped the physical tapestry of the world itself. It has 

also been at the heart of human culture. 

It is because of our intimate relationship with colour that 

people have been wondering for centuries, whether you 

see what I see? 

The answer will tell us not only a great deal about 

how the brain works, but also about who we are as 

individuals and as a society. 

My lab created several unique experiments for a group 

of 150 people of different ages, backgrounds, races and 

sex. Our aim was to see if we all see colour the same. 

What we found really surprised us (though note that our 

findings are just the beginning of the answer). 

In an experiment testing the relationship between 

emotions and colour we discovered that nearly every 

adult assigned yellow to happiness, blue to sadness 

and red to anger (surprise and fear, which are the other 

two universal emotions, had no obvious colour). While 

children showed the same trend, their choices were far 

more mixed and variable. 

On the other hand nearly everyone (young and old) 

showed a similar relationship between colour and 

sound, where lower notes are thought to be best 

represented as dark blue and higher notes as bright 

yellow. 


In other words people seem to have internal mental 

maps between colour and other perceptual qualities, 

such as sound and form. Amazing when these 

relationships do not exist in nature. 

Colour Structures 

In another experiment we asked people to put 49 

coloured blocks on a surface area of 49 spaces. They 

had no other instruction. 

The number of possible images that could have been 

created was 10 raised to the power of 62 - a huge 

number. 

What’s remarkable is that people made patterns that 

were largely predictable, because everyone grouped 

colours together according to similarity. Why? 

Because we have an inherent need for structure, and 

in particular structures that are familiar, in this case 

structures that are similar to the mathematics of the 

images of nature. 

In yet another experiment that really looked at the 

fundamentals of colour vision, we asked whether there 

might be individual differences in simply detecting light. 

What we discovered is that not only are women more 

sensitive than men, but also women who feel they have 

a stronger sense of control are significantly better than 

those women who feel powerless. 

Remarkable really when one remembers that we’re just 

talking about light detection. 

We also examined whether colour can actually alter our 

sense of a minute. 

Our initial observations suggested that a minute takes 

longer for men than for women… about 11 seconds 

longer on average.

Key Research 

But a minute took longer for men and women when 

surrounded by red light, as compared to blue light. 

This effect is likely to be linked to arousal since it is 

well-known that red and blue create different states of 

arousal in men and women alike. 

Deluded Species? 

So we all see the world differently. Indeed, we have no 

choice about this because our experiences of the world 

are necessarily different. 

None of us sees the world as it is. 

In this sense we are all delusional, what each of us sees 

is a meaning derived from our shared and individual 

histories. 

This awareness, possibly more than anything else, 

provides an irrefutable argument for celebrating 

diversity, rather than fear in conformity. 

Which is liberating, since knowing this gives you the 

freedom (and responsibility) to take ownership of your 

future perceptions of yourself and others. 

Reference: LOTTO, B. 2011a. Do you see what I see? [online] Available at: [Accessed 08/08/11].

Key Research 

Horizon: Do you see the same colours as me? BBC blog post by Sophie Robinson 

Back in April this year I was called to a brainstorm with 

the Horizon production team to discuss the science of 

colour. 

It seemed like such a fun and compelling idea and 

addresses the kind of questions we’ve all asked 

ourselves. Do you see the same colours that I see? What 

if what I see as yellow, you really see as blue? And why 

do I fancy you more in red? 

Clearly the intelligent questions of a scientific mind... but 

these really are some of the questions that scientists all 

over the world are asking. And, as the show’s director, 

I jumped at the chance to make this episode and find 

some answers. 

As we looked deeper into the scientific research, 

the more we found that this is a world which is just 

beginning to be properly explored. The scientists 

were bright, curious, often rather quirky, and full of 

fascinating discoveries. 

One of the first people we met was neuroscientist 

Beau Lotto - a master of illusions who wanted to do an 

experiment to find out whether people of different ages, 

gender and nationality see colours in the same way. 

Eight weeks later, there we were with 150 people, 

filming the Beau Lotto colour experiment bonanza. 

The volunteers took part in eight different experiments 

veering from whether colour had an impact on time 

passing, to looking at how people made different 

colour patterns in mosaics, to what emotions people 

associated with different colours - red for anger, blue for 

tranquillity? 

The results shocked even the scientist involved. Beau 

found that colour really can impact the passing of time. 

Volunteers were asked to stand in three different colour 

pods bathed in either blue, red or white light, and Beau 

found that blue light made time pass more quickly and 

red seemed to slow it down. 

“Red makes us highly aware of our environment and so 

time slows down in your mind,” he says. 

Another experiment found that women who are made 

to feel more psychologically powerful and in control 

were more sensitive to spotting changes in colour 

illumination. 

Overall it seemed that depending on the experience we 

bring with us, our perceptions of colour can vary from 

person to person. 

Beau says, “In thinking about ‘do you see what I see’, 

the answer depends on what it is we’re looking at. If 

it’s something that’s shaped by our own individual 

experiences, then we can see the world very differently.” 

We really do perceive colours differently depending on 

experience, age and state of mind. 

Something else we found was that there were scientists 

looking at whether language can influence the way we 

perceive colour. Could the number of words you have 

for colour affect the way you perceive it? 

The only way to find out was to go to a civilisation far 

from the technicolour world we live in, to a tribe who 

have only five words for colour, compared to the 11 

essential colour categories. 

The Himba of northern Namibia - who had never even 

set foot in a local town - call the sky black and water 

white, and for them, blue and green share the same 

word. 

In having fewer words than us for colour, it seems that 

their perception of the world is different to ours - it takes 

them longer to differentiate between certain colours, 

and so we can determine from this that they see the 

world a little differently.

The tribe found us a bit of an oddity - they hadn’t been 

filmed before - so when I played them back the footage 

we had filmed they thought it was the most hysterical 

thing they had every seen. 

And what about the effects colours might have on us? 

Scientists Russell Hill and Iain Greenlees were looking 

into the ‘winning effect’ of the colour red. They 

organised an experiment to see if wearing red might 

have an impact in sport. 

They set up a penalty shoot out with 48 footballers 

looking at whether it was wearing red or seeing red that 

made the difference. 

They found that the men wearing red had lower levels 

of cortisol, the hormone for stress, than those in blue or 

white. This in turn makes them more confident in their 

game. 

These are just a few examples of the people we met and 

filmed. The whole thing was a technicolour experience 

that made us see the world through different eyes - and 

more than that, made us realise there’s more to come. 

This, for once, is a relatively new subject in the world 

of science, so there are many more discoveries to be 

made. 

So when you get up tomorrow, look around you. Think 

about what colours you are going to wear and think 

about the colours you see - do you really see what I see? 

Probably not. 


Reference: ROBINSON, S. 2011. Horizon: Do you see the same colours as me? BBC TV Blog, [blog] 8 August, Available at: [Accessed 09/08/11].

Key Research 

Horizon Episode, Do You See What I See? Screenshots

Reference: Horizon Episode 1. Do You See What I See?, 2011 [Television Programme], BBC, BBC 2, 8 August 21.00. 


Key 

Texts

Key Text 

Chromaphobia David Batchelor 

Reference: BATCHELOR, D., 2000. Chromaphobia. London: Reaktion.

A Scene from the film The Wizard of Oz, in Chromaphobia 

Reference: BATCHELOR, D., 2000. Chromaphobia. London: Reaktion. pp.20 


Key Text 

Chromaphobia – Key Quotes 

p. 10 

‘There is a kind of white that is more than white, and 

this was that kind of white. There is a kind of white that 

repels everything that is inferior to it, and that is almost 

everything. This was that kind of white. There is a kind 

of white that is not created by bleach but that itself 

is bleach. This was that kind of white. This white was 

aggressively white. It did its work on everything around 

it, and nothing escaped.’ 

p. 12 

‘What is it that motivated this fixation with white?’ 

p. 52 

‘If colour is cosmetic, it is added to the surface of things, 

and probably at the last moment. It does not have 

a place within things; it is an after thought; it can be 

rubbed off.’ 

Colour, then, is arbitrary and unreal: mere make-up. But 

while it may be superficial, that is not quite the same as 

being trivial, for cosmetic colour is also always less than 

honest.’ 

‘There is an ambiguity in make-up; cosmetics can often 

confuse, cast doubt, mask or manipulate; they can 

produce illusions or deceptions.’ 

p. 62 

‘In at least one sense, all painting is cosmetic. All 

painting involves the smearing of coloured paste over 

a flat, bland surface, and is done in order to trick and 

deceive a viewer.’ 

‘In one sense, all painting is cosmetic, but in another 

sense the use of flat screen-printed and industrial 

colours will always appear cosmetic – applied, stuck on, 

removable – in a way that the modulated colours and 

tone of oil paint do not.’ 

p. 79 

‘To attend to colour, then, is, in part, to attend to the 

limits of language, what a world without language might 

be like’ 

‘Many commentators have taken the image of childhood 

as a model, if not for a language-free universe then 

at least for a world in which language has not yet fully 

established its grip on experience; this world is, also, 

more often than not, saturated in colour.’ 

‘Stories of adulthood tend more often to lament a world 

of colour eclipsed by the shadow of language; they 

present images of luminous childood becoming clouded 

by the habits of adult life.’ 

‘Elizabeth Barrett Browning: ‘Frequent tears have run the 

colours from my life.’ 

p. 80 

‘If in many of these stories the exposure to language 

robs a life of its colour, are there then other stories in 

which it happens the other way around? Are there equal 

and opposite stories in which exposure to colour robs 

a life of its language, stories in which a sudden flood of 

colour renders a speaker speechless’ 

p. 81 

‘The idea that colour is beyond, beneath or in some 

other way at the limit of language has been expressed in 

a number of ways by a number of writers’ 

Colour and Culture by John Gage 

‘the feeling that verbal language is incapable of defining 

the experience of colour’ 

‘In Colour Codes, Charles A. Riley notes that “ colour 

refuses to conform to schematic and verbal systems’

p. 82 Julia Kristeva 

‘that while ‘semiological approaches consider painting 

a language,’ they are limited insofar as ‘they do not 

allow for an equivalent for colour within the elements of 

language identified by linguistics.’ 

p. 83 Jacqueline Lichtenstein 

‘Colour is ‘ a pleasure that exceeds discursiveness. Like 

passion, the pleasure of coloris slips away from linguistic 

determination’ 

this does not indicate a deficiency in colour so much as 

the insufficiency and impotence of language’ 

‘Whereof we cannot speak, thereof we must remain 

silent’ said Wittgenstein, who also saw in colour the 

outer limits of language. 

p. 84 

‘How often, when it comes to colour – do we revert to a 

gesture? How often do we find ourselves having to point 

to an example of colour? Dulux, a division of Imperial 

Chemical Industries and one of the largest commercial 

paint manufacturers in England, ran a series of television 

advertisements to promote their extensive range of 

household colours. Significantly, they were silent films; 

there was no dialogue in the group of scenes that made 

up each of the short narratives. These were films about 

pointing. … beneath the commercial drive of its surface 

narrative, the stories of the yellow shirt and the lavender 

underpants were also philosophical tales about the 

inadequacy of words. They ask how it is possible for 

us accurately to represent colours to each other, when 

verbal language has proved itself entirely insufficient. 

And they suggest that, almost automatically, we reach 

outside of language with the help of a gesture. We 

point, sample and show rather than say. And in our 

pointing sampling and showing we make comparisons. 

In doing this, we call for the help of something outside 

ourselves and outside language, and in the process we 

expose the limits of our words.’ 


p. 85 

‘They asked how it is possible for us accurately to 

represent colours to each other, when verbal language 

has proved itself entirely insufficient.’ 

‘To fall into colours is to run out of words’ 

‘But there are other ways in which words fail us when 

it comes to colour, and so there are still reasons to 

continue. We have to shift the ground a bit, however, 

and begin to talk less of ‘colour’ and more of ‘colours’. 

What is the difference? If colour is single and colours are 

many, how can we have both?’ 

‘Plotinus said colour is ‘devoid of parts’, and this is 

probably among the most significant things ever said on 

the subject.’ 

Colour was single; it was indivisible. But in being 

indivisible, colour also put itself beyond the reach of 

rational analysis.’ 

‘If colour is indivisible, a continuum, what sense can 

there be in talking of colours? None, obviously… except 

we do it all the time. Colour spreads flows bleeds stains 

floods soaks seeps merges. It does not segment or 

subdivide. Colour is fluid.’ 

‘Colour may be a continuum, but the continuum is 

continuously broken, the indivisible is endlessly divided.’ 

‘From at least the time of Newton, colour has been 

subjected to the discipline of geometry, ordered into an 

endless variety of colour circles, triangles, stars, cubes, 

cylinders or spheres. These shapes always contain 

divisions, and these divisions, as often as not, contain 

words. And with these words colour becomes colours. 

But what does it mean to divide colour into colours? 

Where do the divisions occur? Is it possible that these 

divisions are somehow internal to colour, that they form 

a part of the nature of colour? Or are they imposed on 

colour by conventions of language and culture?

Key Text 

p. 87 

‘Colour has not yet been named’, said Derrida. Perhaps 

not, but some colours have. We have colour names, and 

so we have colours. 

The human brain can distinguish minute variations 

in colour; it has been said that we can recognize 

several million different colours. At the same time, in 

contemporary English, there are just eleven general 

colour names in common usesage: Black, white, red, 

yellow, green, blue, brown, purple, pink, orange, grey. 

p. 88 

Literary Welsh , for example has no words that 

correspond exactly with the English ‘green’, ‘blue’, ‘grey’ 

and ‘brown’; Vietnamase and Korean make no clear 

distinction between green and blue; and Russian has no 

single word for blue, but two words denoting different 

colours. Then there is purple. Newton had a problem 

with it, which we will return to, and so do the French, 

a language which, like English, also scores the full 

eleven on the Belin-Kay scale. But if the French violet 

corresponds to our ‘violet’, it would seem that it is not 

quite the same as our purple. Likewise, their brun might 

more or less correspond to our ‘brown’, at least in the 

abstract, as a colour term; but when used descriptively 

rather than referentially, when applied to things in the 

world like shoes, hair and eyes, brown and brun part 

company. French shoes may be brown, but they aren’t 

brun so much as marron. And French hair, if it’s brun, is 

dark rather than brown. 

‘Derek Jarman: ‘This morning I met a friend on the 

corner of Oxford Street. He was wearing a beautiful 

yellow coat. I remarked on it. He had bought it in Tokyo 

and he said it was sold to him as green.’ 20 

p. 89 

‘For Lyons, the lesson of Hanunoo and other languages 

is that colour names are so tired into cultural usage of 

one kind or another that any abstract equivalence is 

effectively lost. In some cases they cease to be colour 

names in the ordinary sense. To conceive of colour in 

terms of independent of, say luminosity or reflectiveness 

is in itself a cultural and linguistic habit and not a 

universal occurrence. Ditto the separation of hue from 

tone.’ 

Such basic colour terms as we have, to put it another 

way, even terms like ‘colour’, are the products of 

language and culture more than the products of colour.’ 

p. 90 

‘the names of colours, in themselves, have no precise 

chromatic content: they must be viewed within the 

general context of many interacting semiotic systems.’ 

‘Russian, we are told has two words for blue. That is to 

say, Russians appear to deal with blue in roughly the 

way we deal with red and pink. Certainly, what we call 

light blue is optically distant from dark blue as pink is 

from red, perhaps more to, and yet our language allows 

no such independence for bits of blue. ‘Pink’ is the only 

basic colour term in English that also denotes a specific 

part of another basic colour term, one end of ‘red’. But 

there seems to be no necessary reason for this in terms 

of our experience of colour. When we see light blue, do 

we see something different from what a Russian speaker 

sees? And while we are on the subject of light and dark, 

what about dark yellow? Yellow is certainly the lightest 

of the spectrum colours, but when yellow is darkened, 

where does it go? Does it get wrapped up in a kind of 

brown? Or is it lost to the insecure empire of orange? 

And if we can just about imagine yellow drifting and 

darkening towards orange and brown, why can’t we 

imagine it turning towards green in the same way? What 

happens to yellow as it travels towards green? And how 

distinct is green from yellow? More distinct than orange 

is from yellow and purple s from blue and from red? 

Probably, but then why don’t we have a name or names 

for the colour space between green and yellow?

p. 91 

‘For the philosopher C. L. Hardin, the author of one 

of the most comprehensive and rigorous studies of 

the science of colour, this gap, this nameless colour 

between yellow and green, remains an anomaly (as does 

the entire existence of pink, incidentally). Nevertheless, 

he offers a tentative and, for a philosopher-scientist, a 

rather weird explanation for this space: he thinks it is not 

a very nice colour, or that people tend not to like it, so 

nobody has bothered to name it.’ 

‘Wittgenstein asked: ‘How do I know this colour is red?’ 

To which he replied: ‘… because I have learned English.’ 

27 

p. 92 

‘William Gass on the relationship between colour names 

and colours, starting with blue: 

The word itself has another colour. It’s not a word with 

any resonance, although the e was once pronounced. 

There is only a bump now between the b and l, the relief 

at the end, the whew. It hasn’t the sly turn which crimson 

takes halfway though, yellow’s deceptive jelly, or the 

rolled down sound in brown. It hasn’t violet’s rapid 

sexual shudder, or like a rough road the irregularity of 

ultramarine, the low puddle un mauve like a pancake 

covered in cream, the disapproving purse to pink, the 

assertive brevity of red, the whine of green.’ 28 

Arthur Rimbaud 

‘I invented the colour of vowels! – A black, E white, I red, 

O blue, U green.’ 29 

‘To discuss colour terms is, it seems, to talk about 

language more than it is to talk about colour.’ 

‘Basic colour terms may be universal, but they are 

useless when it comes to the study of colour.’ 

‘Gass’s beloved blue is everywhere, and everywhere it is 

different. The word blue holds the entire disorganized 

and antagonistic mass of blues in a prim four-lettered 

cage.’ 

Reference: BATCHELOR, D., 2000. Chromaphobia. London: Reaktion. 


p. 98 

1964 radio interview Frank Stella 

‘I knew a wise-guy who used to make fun of may 

paintings, but he didn’t like the Abstract Expressionists 

either. He said the would be good painters if they could 

only keep the paint as good as it was in the can.’ 

‘To keep the paint as good as it was in the can’ Frank 

Stella 

p. 99 

‘The change in art it acknowledges may not seem so big: 

it says that paint now comes from a can. That is, from 

a can rather than from a tube: whereas artists’ paints 

usually come in tubes, industrial or household paints are 

normally stored in cans. Artists’ paints were developed 

to allow the representation of various kinds of bodies 

in different types of space. ‘Flesh was the reason oil 

painting was invented’, said De Kooning. Industrial 

paints are made to cover large surfaces in a uniform 

layer of flat colour. They form a skin, but they do not 

suggest flesh.’ 

‘Not only did this type of paint come in a can, it looked 

good in the can.’ 

‘The anxiety that Stella’s remark betrays does not, or at 

least does not directly, concern the loss of three or more 

centuries of oil and easel painting. He was pointing in 

another direction. His concern was not how his work 

would measure up to the art of the past but how it 

would compare with the paint in the can.’ 

p. 100 

‘That Stella sought to ‘keep’ the paint that ‘good’ 

suggests he knew it might be hard to improve on the 

materials in their raw state, that once the paint had been 

put to use in art, it might well be less interesting than 

when it was ‘in the can’.’

Key Text 

Other Writing by David Batchelor 

Chromophobia: Ancient and Modern, and a Few Notable Exceptions 

To address the question of colour in art is, sooner or 

later, to encounter a strange and deep kind of loathing. 

For example: 

The union of design and colour is necessary to beget 

painting just as is the union of man and woman to beget 

mankind, but design must maintain its preponderance 

over colour. Otherwise painting speeds to its ruin: it will 

fall through colour just as mankind fell through Eve. 1 

The passage was written by the influential nineteenthcentury 

critic and colour theorist, Charles Blanc, it is 

interesting on a number of counts. First he identifies 

colour with the ‘feminine’ in art; second he asserts the 

need to subordinate colour to the ‘masculine’ discipline 

of design, or drawing; third he exhibits a reaction typical 

of phobics: a massive overvaluation of the power of 

that which he fears; and fourth he says nothing at all 

original. In aesthetics and art theory colour is very often 

ascribed either a minor, a subordinate, or a threatening 

role. The devaluation of colour expressed in the phrase 

disegno versus colore has a very long history. The idea 

that adequate representation through line alone is both 

possible and preferable was revived during the Italian 

Renaissance from ancient Greek and Roman sources, 

and continued to inform academic training until the 

nineteenth century. 2 When colour was admitted to the 

equation of art it was, as Blanc indicated, usually within 

a strongly disciplinarian regime marshalled by the more 

‘profound’ art of drawing. Even Kant, writing in 1790, 

maintained that while colours may give ‘brilliancy’ and 

‘charm’ to painting and sculpture, ‘make it really worth 

looking at and beautiful they cannot’. Again, only 

drawing was ‘essential’. 3 

Chromophobia, the fear of corruption through 

colour, is not inherent in all devaluations of colour 

in aesthetics, but it is visible in many instances. 

Associated with decadence, exoticism, confusion, lack 

of clarity, superficiality and decoration, colour has been 

conscripted into other more well-documented racial and 

sexual phobias. Taken as a marker of the feminine by 

Blanc, others as far back as Pliny have placed colour on 

the ‘wrong’ end of the rhetorical opposition between 

the Occidental and the Oriental, the Attic and the Asian, 

the rational and the irrational. For Aristotle, colour was 

a drug (‘pharmakon’); in rhetoric itself ‘colores’ came 

to mean embellishment of the essential structure of an 

argument. If colour is not a contaminant, then it is more 

often than not treated as an addition, embellishment or 

supplement, relating to ‘mere’ appearances rather than 

to the essential structure of things. 

A suspicion of colour persists in certain types of art, 

particularly the kind which aligns itself with the more 

cerebral, intellectual and moral aspects of experience. 

A commitment to one or another variety of Realism has 

almost always been marked by a fondness for brown; 

Conceptual Art made a fetish of black and white. To this 

day, ‘seriousness’ in art is usually available only in shades 

of grey. The idea that strong colour is the preserve of 

primitives and children may not be stated much these 

days, but it appears still to have a strong silent presence. 

One of the reasons for the continued devaluation of 

colour in much art and theory is perhaps that both 

conceptually and practically it is extraordinarily hard 

to contain. Conceptually colour has proved irresistibly 

slippery, constantly evading our attempts to organise it 

in language or in a variety of linear, circular, spherical, 

or triangular geometries. For Plotinus colour was simply 

‘devoid of parts’ and therefore (literally) beyond analysis. 

When the twenty-two-year-old Newton revolutionised 

the scientific understanding of light and colour, the 

subordination of colour to a system of laws also 

became an imperative. But the rationale for Newton’s 

division of the spectrum or rainbow into seven primary 

colours was based less on any inherent divisions within 

the colour continuum that on the desire to make it 

match the seven distinct notes in the musical scale. 4 

Evidence of the sheer contingency of colour systems 

and colour concepts has been produced by a number

of ethnographers, linguists and cultural historians in 

recent decades. But only a relatively few philosophers 

and theorists have found the awkwardness of colour at 

all suggestive. Kierkegaard identified intense colour with 

childhood, as others had before him, but lamented its 

loss: ‘The hues that life once had gradually became too 

strong, too harsh for our dim eyes’. 5 The problems of 

matching the experience of colour with available colourconcepts 

became the basis of Wittgenstein’s last main 

preoccupation. 6 For Barthes, colour, like other sensory 

experiences, could only be addressed in language in 

terms of metaphor: his answer to the question ‘what is 

colour?’ was: ‘a kind of bliss’. Barthes’ sensualising, or 

rather his eroticising, of colour is a very striking inversion 

of Blanc’s Old Testament foreboding. In a way there is 

no disagreement between them: colour has a potency 

which will overwhelm the subject and obliterate all 

around it, even if, for Barthes, this was only momentary, 

‘like a closing eyelid, a tiny fainting spell’. 7 The potency 

of colour presents some real problems for artists: colour 

saturation tends to knock out other kinds of detail in 

a work; it is difficult to make it conform to the spatial 

needs of bodies, be they abstract or figurative; it tends 

to find its own level, independent of what is around 

it; colour is, in short, uncooperative. The advent of 

monochrome painting during the 1950s and ‘60s might 

seem like a logical, if extreme, solution to this difficulty: 

here, for once, colour would not have to cooperate 

with drawing. And yet, with some important exceptions 

(such as Klein and Fontana), monochrome painting has 

often proved oddly shy of chromatic intensity, preferring 

instead of the quieter waters of tonal value and variation 

(Ryman, Richter, Charlton, for example). The reasons 

for this are various and complex; but they may have 

something to do with painting’s unavoidable relationship 

with the (usually white) wall-plane and the need to tune 

painting to this given of the gallery environment. And 

this, in turn, may explain, in part, why in many instances 

during the post-war period a preoccupation with colour 

has found its form in sculpture and three-dimensional 

work. But there are also other, stronger, reasons for this 

change of direction and dimension. 


In his final, posthumously published, essay ‘Some 

Aspects of Colour in General and Red and Black in 

Particular’ (1994), Donald Judd remarked that, after 

Abstract Expressionism, ‘colour to continue, had to 

occur in space’. 8 He also indicated that, in a series of 

multi-coloured works he began in the early ‘80s, he 

wanted ‘all of the colours to be present at once’. In 

painting, even in ‘flat’ abstract painting, colour will tend 

to function pictorially, to advance or recede relative 

to other colours, and to detach itself from the picture 

plane. In sculpture such as Judd’s, colours are stabilised 

by being present as literal surfaces of three-dimensional 

elements. The edge of each colour coincides with the 

edges of the object, and being visibly assembled from 

discrete units, this leaves less room for optical jockeying, 

and thus more space for a very wide variety of colours 

‘to be present at once’. These works – a large number 

were made during the second half of the 1980s, and 

many more u assembled works remained in Judd’s 

studio after his death – are perhaps the ‘purest’ colour 

experiments in recent sculpture. The aim, I think, was no 

more and no less than an open-ended investigation into 

the possibilities of colour combination. Derived from 

the industrial colour-chart, rather than the traditional 

colour-circle, Judd’s work freed itself from the baggage 

of traditional colour-theory, with its prescriptions, its 

grammar of opposites and its hierarchies of primaries, 

secondaries and tertiaries. It also made colour a kind of 

Readymade, something to be selected from a stockist, 

like the range of light-industrial materials which are 

typical of all Judd’s work. 

For a number of other sculptors, particularly during the 

1960s, the materials and processes of industry offered 

a world to work with. While this is well documented, 

what is often overlooked is how the colours of industrial 

materials and commercial finishes focused the artists’ 

attention. John Chamberlain has used the applied spray 

painted colour of cellulose car-paints since the early 

1960s; Carl Andre’s wide range of metal sculptures 

are marked by strong intrinsic differences in colour

Key Text 

which he sometimes explores within a single work; and 

Dan Flavin worked with the palette of commercially 

available colours in fluorescent light, again either singly 

or in combination. There are obvious and important 

differences between these types of work: in some 

cases colour is applied, in others it is intrinsic to the 

material; in the case of Flavin, and in some works by 

Judd in which he employs transparent acrylic sheet, 

colour is a quality of light emitted from, and reflected 

in, the surfaces of the work. But what all this work has in 

common is a fascination with the colours and surfaces of 

modernity; and it is from these sources – from the slick 

and brash and vulgar world of industry and commerce, 

rather than the more refined and repressed taste of 

high culture – that these artists, like their contemporary 

Warhol, find the most vivid material. 

This work is alike also in that it is usually assembled or 

arranged from pre-existing parts. Except in the case 

of Chamberlain, the materials are not manipulated in 

the studio so much as ordered-up from the stockists 

or fabricators. In most cases the materials are also flat 

unmodulated planes rather than solids (Andre being 

the partial exception here); and this has consequences 

for the experience of colour in the work. Built rather 

than crafted, joined together rather than whole, literal 

rather than pictorial, planar rather than solid, synthetic 

rather than organic, regular rather than irregular: 

the characteristics of this art are also characteristics 

of our modernity. And the colours of modernity are 

inseparable from these other aspects: its surfaces, its 

shapes, its structures and its arrangements. 

It may be the case that the most important and 

experimental use of colour occurs outside the world of 

high art. This offers the interested artist a vast resource 

of readymades and references, from the developments 

of the automobile industry and commercial paint 

manufacturers, to the patterns on toys, ornaments, 

packaging and departure-lounge kitsch. If the New 

York artists of the 1960s tended to draw on a range 

of industrial commodities for their work, several 

American and European artists over the last decade 

have exploited the ubiquitous world of consumer 

commodities. Mike Kelley and Sylvie Fleury have both 

made direct use of such material, and even if their work 

is not ostensibly about colour, the point is that once this 

commodity world is invoked, colour invariably comes 

with it and makes itself present. Some of the early work 

of Tony Cragg may have been made in recognition of 

this. Plastic Palette II (1989), is, like all his work of the 

time, made from the detritus of commerce and industry, 

the found shards of brightly coloured plastic which 

insert themselves into the corners of our cities. In this 

instance the shards’ colours become the organising 

point for the work’s imagery – a silhouette of the 

traditional painter’s palette – and this, in turn, becomes 

an acknowledgement of the ambiguous position – 

between painting and sculpture – which is characteristic 

of much recent colour-based work, and Judd’s and 

Flavin’s in particular. 

Colour as a market of mass-culture or of kitsch is also 

represented in the work of artists such as Jenny Holzer 

and Jeff Koons. In Holzer’s case the use of lightemitting 

materials is transformed from the simple and 

static geometry of a Flavin to a hyperactive spectacle 

of sound-bites and non-sequiturs. Koons’ carved 

and painted flower arrangements (and his animals 

and figures) make a positive, if ironic, value of much 

which aesthetics has suppressed: the ornamental, the 

decorative, the domestic, the trivial, the childlike. Note, 

however, that it is not only through intense colour 

that Koons invokes the kitsch: he has also succeeded 

in hijacking white, the last colour in the fortresses 

of refinement, in works which invoke nothing more 

elevated than domestic cleanliness or neo-classical 

sentimentality. 

None of the above constitutes anything like a school 

or a movement. If there is a preoccupation in the work 

mentioned so far with some or other aspect of mass

culture, the particular reasons for this preoccupation 

will be quite different for each artist. And not all 

sculptors who thematise colour have done so by this 

route. Anish Kapoor and Georg Baselitz both make 

work in a relatively traditional way (as does Koons, 

sometimes), by carving or otherwise cutting into lumps 

of wood or stone; and this immediately distinguishes 

their work from the work discussed so far. In Kapoor’s 

and Baselitz’s case, the worked material is then either 

partially or entirely covered with pigment. And in each 

case the relationship of the colour to the material is 

crucial, seeming to dissolve it into a perceptual vapour 

(Kapoor) or organise it into a loosely conceived figure 

(Baselitz). In neither case is there anything in the finished 

work which readily links it with the world of commerce 

and industry. Rather the opposite: both sculptors seem 

to be involved in a turn away from modernity; but in 

making this turn, coincidentally, they arrive at a point not 

that remote from some of the other work mentioned so 

far. In the rhetoric of chromophobia, the vulgar and the 

kitsch are in the same domain as the primitive and the 

exotic – the critical and self conscious reference-points 

of Baselitz and Kapoor respectively. 

The problems for an artist working with colour are not 

only the chromophobic prejudices, whereby colour 

is positioned, in one way or another, as Other to the 

higher values of Culture; but also the handed-down 

baggage of traditional colour-theory, within which 

colour is marshalled, disciplined, subordinated, and 

subject to entirely arbitrary divisions and codes of 

conduct. In some respects, chromophobia is preferable 

to the systemisation of colour: at least chromophobes 

recognise and value, in a perverse back-to-front way, 

colour’s potency and indiscipline. Only relatively 

few artists actively thematise colour these days; and 

those who do (a fuller list would include painters, 

photographers and installation artists of various types), 

do so in an informal and highly idiosyncratic way. To 

talk of colour in recent art is to speak of instances and 

highly localised interests; not of systems, models and 


movements. This is the point: colour is infinitely more 

complex than the means we have to describe it; and in 

that space between seeing and knowing there may be 

occasional moments of freedom. 

Published by the Centre for the Study of Sculpture to 

accompany ‘The Colour of Sculpture’ at The Henry 

Moore Institute, 12 December 1996 – 6 April 1997 and 

‘Polymonochromes’, an exhibition of works by David 

Batchelor in the Study Galleries of Leeds City Art 

Gallery, 17 January – 6 April 1997. 

References 

1 Charles Blanc, cited in Charles A Riley II, Colour Codes, 

Hanover and London, University of New 

England Press, 1995, p.6. 

2 See John Gage, Colour and Culture, London, Thames 

and Hudson, 1993, esp. chapters 1 and 7. 

3 Cited in Riley, Colour Codes, op cit, p.17. 

4 Malcolm Longair, ‘Light and Colour’, in Colour: Art & 

Science, ed. Trevor Lamb and Janine 

Bourriau, Cambridge University Press, 1995, pp.65-102. 

5 Cited in Riley, Colour Codes, op cit, p.17. 

6 See Ludwig Wittgenstein, Remarks on Colour, Oxford, 

Basil Blackwell, 1977. 

7 Cited in Riley, Colour Codes, op cit, pp.59-60. 

8 Donald Judd, ‘Some Aspects of Colour in General and 

Red and Black in Particular’, Artforum, 

Vol.XXXII, no.10, Summer 1994, pp.70-79, 110 and 113. 

Reference: BATCHELOR, D. 1997. Chromophobia: Ancient and Modern, and a Few Notable Exceptions. [online] Available at: [Accessed 17/04/11].

Referencing Colours 

Dulux – Colour Sampling Television Advert Campaign 2008, Referenced in Chromaphobia 

Reference: Njm1971nyc, 2009. ICI Dulux - “Snip” & “Baby” - UK tv ads. [video online] Available at: [Accessed 

04/08/2010].


Reference: Njm1971nyc, 2009. ICI Dulux - “Snip” & “Baby” - UK tv ads. [video online] Available at: [Accessed 

04/08/2010].

Key Text 

Colour by Gavin Ambrose and Paul Harris 

Reference: AMBROSE, G., & HARRIS, P., 2005. Colour. Lausanne: AVA.

Key Quotes 

p. 6 

Colour is the most immediate form of non-verbal 

communication. We naturally react to colour as we 

have evolved with a certain understanding of it, partly 

because the survival of our ancestors depended on it 

with regard to what to consume and avoid. 

p. 11 

Colour is perhaps the first element that we register 

when we view something for the first time. Our cultural 

development and conditioning mean that we will 

naturally make associations based upon the colours 

we see., and these provide and idea of how we should 

react to an object or design that incorporates them. 

p. 18 

Every colour corresponds to a unique light wavelength, 

but a list of different wavelength values does not 

provide a particularly useful description of a colour. 

Similarly, the names of different colours have 

descriptive limits: what does dark red actually mean? 

p. 166 

The English language contains many idioms. ... a 

large selection of these are colour related. Although 

true meaning can be understood from the individual 

elements of the phrase, the colour expression 

often hints at the intended meaning. The different 

associations we have made with each colour have been 

absorbed into our language, so they are frequently 

used to reinforce the emotion or mood someone is 

trying to convey. 

Reference: AMBROSE, G., & HARRIS, P., 2005. Colour. Lausanne: AVA. 


Key Text 

The Meaning of Colours 

Reference: AMBROSE, G., & HARRIS, P., 2005. Colour. Lausanne: AVA. pp.12-13

Primary Colours, Tertiary Colours and Colour Mixing 

Reference: AMBROSE, G., & HARRIS, P., 2005. Colour. Lausanne: AVA. pp.17 & 19 


Key Text 

Colour Combinations 

Reference: AMBROSE, G., & HARRIS, P., 2005. Colour. Lausanne: AVA. pp.20-21

Colour by Edith Anderson Feisner 

Reference: ANDERSON FEISNER, E., 2006. Colour : how to use colour in art and design. London : Laurence King. 


Key Text 

Remarks on Colour by the Philosopher Ludwig Wittgenstein, 

Written in Response to Goethe’s Theory of Colours 

Reference: WITTGENSTEIN, L., 1979. Remarks on colour. Oxford : Blackwell.

Pages from the Book Remarks on Colour by the Philosopher Ludwig Wittgenstein, 

Written in Response to Goethe’s Theory of Colours 


Key Text 

Key Quotes 

p. 2 

Lichtenberg says that very few people have ever seen 

pure white. So do most people use the wrong word, 

then? And how did he learn the correct use? — He 

constructed an ideal use from the ordinary one. And 

that is not to say a better on, but one that has been 

refined along certain lines and in the process something 

has been carried to extremes. 

If I say a piece of paper is pure white, and if snow 

were placed next to it and it then appeared grey, in 

its normal surroundings I would still be right in calling 

white and not light grey. It could be that I use a more 

refined concept of white in, say, a laboratory (where, for 

example, I also use a more refined concept of precise 

determination of time). 

What is there in favour of saying that green is a primary 

colour, not a blend of blue and yellow? Would it be right 

to say: “ You can only know it directly by looking at the 

colours”? But how do I know that I mean the same by 

the words “primary colours’ as some other person who 

is inclined to call green a primary colour? No, —here 

language-games decide. 

p. 3 

Someone is given a certain yellow-green (or blue-green) 

and told to mix a less yellowish (or bluish) one — or 

to pick it out from a number of colour samples. A less 

yellowish green, however, is not a bluish one (and vice 

versa), and there is also such a task as choosing, or 

mixing a green that is neither yellowish nor bluish. I say 

“or mixing” because a green does not become both 

bluish and yellowish because it is produced by a kind of 

mixture of yellow and blue. 

Even if green is not an intermediary colour between 

yellow and blue, couldn’t there be people for whom 

there is bluish-yellow, reddish -green? I.e. people for 

whose colour concepts deviate from ours — because, 

after all, the colour concepts of colour-blind people too 

deviate from those of normal people, and not every 

deviation from the norm must be blindness, a defect. 

Someone who is familiar with reddish-green should be 

in a position to produce a colour series which starts with 

red and ends in green and which perhaps even for us 

constitutes a continuous transition between the two. We 

would then discover that at the point where we always 

see the same shade, e.g. of brown, this person some 

times sees brown and sometimes see reddish green. It 

maybe, for example, that he can differentiate between 

the colours of two chemical compounds that seem to us 

to be the same colour and he calls one brown and the 

other reddish-green. 

p. 4 

Imagine a tribe of colour-blind people, and there could 

easily be one. They would not have the same colour 

concepts as we do. For even assuming they speak, e.g. 

English, and thus have all the English colour words, they 

would still use them differently than we do and would 

learn to use them differently. Or if they have a foreign 

language, it would be difficult for us to translate their 

colour words into ours. 

But even if there were also people for whom it was 

natural to use the expressions “reddish-green” or 

“yellowish-blue” in a consistent manner and who 

perhaps also exhibit abilities which we lack, we would 

still not be forced to recognise that they see colours 

which we do not see. There is, after all, no commonly 

accepted criterion for what a colour is, unless it is one of 

ours colours. 

p. 7 

We speak of the ‘colour of gold’ and do not mean 

yellow. “Gold-coloured” is the property of a surface that 

shines or glitters. 

p. 10 

The fact that I can say this place in my visual field is 

grey-green does not mean that I know what should be 

called an exact reproduction of this shade of colour.

p. 11 

Loo at your room late in the evening when you can 

hardly distinguish between colours any longer ¬— and 

now turn on the light and paint what you saw earlier in 

the semi-darkness. — How do you compare the colours 

in such a picture with those of the semi-dark room? 

When we’re asked “What do the words ‘red’, ‘blue’, 

black”, “white mean?” We can, of course, immediately 

point to things which have these colours, – but our 

ability to explain the meanings of these words goes no 

further! For the rest, we have either no idea at all of their 

use, or a very rough and to some extent false one. 

Goethe’s theory of the constitution of the colours of 

the spectrum has not proved to be an unsatisfactory 

theory, rather it really isn’t a theory at all. Nothing can 

be predicted with it. It is, rather, a vague schematic 

outline of the sort we find in James’s psychology. Nor is 

there any experimentum crucis which could decide for 

or against the theory. 

p. 13 

There could be people who didn’t understand our way 

of saying that orange I a rather reddish-yellow, and 

who would only be inclined to say something like that 

in cases where a transition from yellow, through orange 

to red took place in front of there eyes. And for such 

people the expression “reddish-green” need present no 

difficulties. 

p. 17 

But pure yellow too is lighter than pure, saturated red, 

or blue. And is this proposition a matter of experience? 

— I don’t know, for example, whether red (i.e. pure red) 

is lighter or darker than blue; to be able to say, I would 

have to see them. And yet, if I had seen them I would 

know the answer once and for all, like the result of an 

arithmetical calculation. 


p. 19 

The question will be, e.g.: can you teach the meaning 

of “saturated green” by teaching the meaning of 

“saturated red”, or “yellow”, or “blue”? 

Something that may make us suspicious is that some 

people have thought they recognized three primary 

colours, some four. Some have thought green to be an 

intermediary colour between blue and yellow, which 

strikes me, for example, as wrong, even apart from 

any experience. Blue and yellow, as well as yellow and 

green, seem to be opposites — but perhaps that is 

simply because I am used to seeing them at opposite 

points on the colour circle. 

p. 20 

Ask this question: Do you know what “reddish” means? 

And how do you show that you know it? Languagegames: 

“Point to a reddish yellow (white, blue, brown) — 

“Point to an even more reddish one” — “A less reddish 

one” etc. 

Now you have mastered this game you will be told 

“Point to a somewhat reddish-green” Assume there are 

two cases: Either you do point to a colour (and always 

the same one), perhaps to an olive green — or you say, 

“I don’t know what that means,” or “There’s no such 

thing.” 

p. 22 

To what extent can we compare black and white to 

yellow, red, and blue, and to what extent can’t we? 

My feeling is that blue obliterates yellow, — but why 

shouldn’t I call a somewhat greenish yellow a “bluish 

yellow” and green an intermediary colour between blue 

and yellow, and a strongly bluish green a somewhat 

yellowish blue? 

In a greenish yellow I don’t notice anything blue. — For 

me, green is one special way-station on the coloured 

path from blue to yellow, and red is another.

Key Text 

p. 23 

What does it mean to say, “Brown is akin to yellow?” 

Does it mean that the task of choosing a somewhat 

brownish yellow would be readily understood? (Or a 

somewhat more yellowish-brown)> 

“Yellow is more akin to red than blue.” 

It is a fact that we can communicate with one another 

about the colours of things by means of six colour 

words. Also, that we do not use the words “reddishgreen”, 

“yellowish-blue” etc. 

p. 25 

In everyday life we are virtually surrounded by impure 

colours. All the more remarkable that we have formed a 

concept of pure colours. 

Why don’t we speak of a ‘pure’ brown? is the reason 

merely the position of brown with respect to the other 

‘pure’ colours, its relationship to them all? — Brown is, 

above all, a surface colour, i.e. there is no such thing 

as a clear brown, but only a muddy one. Also: brown 

contains black — (?)— How would a person have to 

behave for us to say of him that he knows a pure, 

primary brown? 

We must always bear in mind the question: How do 

people learn the meaning of colour names? 

What does “Brown contains black,” mean? There are 

more and less blackish browns. Is there one which 

isn’t blackish at all? There certainly isn’t one that isn’t 

yellowish at all. 

And we must not forget either that our colour words 

characterize the impression of a surface over which our 

glance wanders. That’s what they are there for. 

And indeed the pure colours do not even have special 

commonly used names, that’s how unimportant they are 

to us. 

p. 27 

The indefiniteness n the concept of colour lies, above 

all, in the in definiteness of the concept of the sameness 

of colours, i.e. of the method of comparing colours. 

What makes grey a neutral colour? 

What makes bright colours bright? 

Why don’t we include black and white in the colour 

circle? 

Grey is between two extremes (black and white), and 

can take one the hue of any other colour. 

p. 29 

From the fact that this table seems brown to everyone, 

it does not follow that it is brown. But just what does it 

mean to say, “This table isn’t really brown after all”? — 

So does it then follow from its appearing brown to us, 

that it is brown? 

p. 30 

If you are not clear about the role of logic in colour 

concepts, begin with the simple case of, e.g. a yellowish 

red. This exists, no one doubts that. How do I learn the 

use of the word “yellowish”? Through language games 

in which, for example things are put in a certain order. 

Thus I can learn, in agreement with other people, to 

recognize yellowish and still more yellowish red, green, 

brown and white. 

I say blue-green contains no yellow: if someone else 

claims that it certainly does contain yellow, who’s right? 

How can we check? Is there only a verbal difference 

between us? — Won’t one recognise a pure green that 

tends neither towards blue nor toward yellow? 

p. 31 

If someone had called this difference between green 

and orange to Runge’s attention, perhaps he would 

have given up the idea that there are only three primary 

colours.

p. 32 

Someone who agrees with Goethe finds that Goethe 

correctly recognised the nature of colour. And here 

‘nature’ does not mean a sum of experiences with 

respect to colours, but it is to be found in the concept of 

colour. 

p. 33 

‘The colours’ are not thing that have definite properties, 

so that one could straight off look for or imagine colours 

that we don’t yet know, or imagine someone who knows 

different ones than we do. It is quite possible that, under 

certain circumstances, we would say that people know 

colours that we don’t know. 

p. 34 

I may have impressed a certain grey-green upon my 

memory so that I can always correctly identify it without 

a sample. Pure red (blue, etc) however, I can so to speak 

always reconstruct. It is simply a red that tends neither 

to one side nor the other. 

p. 40 

What is the experience that teaches me that I 

differentiate between red and green? 

p. 43 

Explaining colour words by pointing at coloured pieces 

of paper does not touch on the concept of transparency. 

It is this concept that stands in unlike relations to the 

various colour concepts. 

p. 46 

Why can’t we imagine a grey-hot? Why can’t we think of 

it as a lesser degree of white-hot. 

p. 48 

If we taught a child the colour concepts by pointing to 

coloured flames or coloured transparent bodies, the 

peculiarity of white grey and black would show up more 

clearly. 


It is easy to see that not all colour concepts are logically 

of the same kind. It is easy to see the difference 

between the concepts: “the colour of gold” or ‘ the 

colour of silver’ and ‘yellow’ or ‘grey’. But it is hard to 

see that there is a somewhat related difference between 

‘white’ and ‘red’. 

p. 49 

Whether I see something as grey or white can depend 

upon how I see things around me illuminated. To me in 

one context the colour is white in poor light, in another 

it is grey in good light. 

p. 50 

Our colour concepts sometimes relate to substances 

(Snow is white), sometimes to surfaces (this table is 

brown), sometimes to the illumination (in the reddish 

evening light), sometimes to transparent bodies. 

To be able generally to name a colour, is not the same 

as being able to copy it exactly. I can perhaps say 

“There I see a reddish place” and yet I can’t mix a colour 

that I recognize as being exactly the same. 

I give a colour the names “F” and I say it is the colour 

that I see there. Or perhaps I paint my visual image and 

then simply say “I see this”. Now, what colour is at this 

spot in my image? How do I determine it? I introduce, 

say the word “cobalt blue”: How do I fix what ‘C’ is? I 

could take as the paradigm of this colour a paper or the 

dye in a pot. How do I now determine that a surface (for 

example) has this colour? Everything depends on the 

method of comparison. 

p. 51 

What we call the “coloured” overall impression of a 

surface is by no means a kind of arithmetical means of 

all the colours of the surface. 

I would like to say “this colour is at this spot in my visual 

field (completely apart from any interpretation)”. But 

what would I use this sentence for? “This” colour must

Key Text 

(of course) be one that I can reproduce. And it must be 

determined under what circumstances I say something is 

this colour. 

Imagine someone pointing to a spot in the iris in a face 

by Rembrandt and saying “the wall in my room should 

be painted this colour. 

I paint the view from my window; one particular spot, 

determined by its position in the architecture of a 

house, I paint ochre. I say “I see this spot in this colour.” 

That does not mean that I see the colour ochre at this 

spot, for this pigment may appear much lighter or darker 

or more reddish (etc.) than ochre, in these surroundings. 

I can perhaps say “ I see this spot the way I have painted 

it here (with ochre); but it has a strongly reddish look to 

me.” But what if someone asked me to give the exact 

shade of colour that appears to me here? How should 

I describe it and how should I determine it? Someone 

could ask me, for example, to produce a colour sample, 

a rectangular piece of paper of this colour. I don’t say 

that such a comparison is utterly uninteresting, but it 

shows that it isn’t from the outset clear how shades 

of colour are to be compared, and therefore, what 

“sameness of colour” means here. 

p. 52 

One might be inclined to believe that an analysis of our 

colour concepts would lead ultimately to the colours of 

places in our visual field, which would be independent 

of any spatial or physical interpretation. 

p. 56 

When blind people speak, as they like to do, of blue sky 

and other specifically visual phenomena, the sighted 

person often says “ who knows what he imagines that 

to mean” — But why doesn’t he say this about other 

sighted people? It is, of course, a wrong expression to 

begin with. 

Reference: WITTGENSTEIN, L., 1979. Remarks on colour. Oxford : Blackwell. 

p. 61 

To observe is not the same thing as to look at or to view. 

“Look at this colour and say what it reminds you of”. If 

the colour changes you are no longer looking at the one 

I meant. One observes in order to see what one would 

not see if one did not observe. 

We say for example “Look at this colour for a certain 

length of time”. But we don’t do that in order to see 

more than we had seen at first glance.

Colour and Meaning by John Gage 

Reference: GAGE, J., 1999. Colour and Meaning, art science symbolism. Thames and Hudson : London. 


Key Text 

White by Kenya Hara 

Reference: HARA, K., 2007. White. Baden: Lars Muller Publishers. 

Key Quotes 

p. 3 

‘Is white a colour? It is like a colour, yet at the same time 

we can also conceive of it as a noncolour. What then, we 

must ask, is colour in the first place?’ 

‘Things like the rich golden yellow of the yolk from a 

broken egg, of the colour of tea brimming in a teacup, 

are not merely colours; rather they are perceived at 

a deeper level through their texture and their taste, 

attributes inherent in their material nature.’ 

p. 4 

‘Colours do not exist separately and independently 

within nature; they are constantly shifting in response 

to subtle gradations of light. It is language that, 

magnificently gives them clear shape’ 

‘We absorb the rightness of those linguistic choices at 

the emotional level.’ 

‘The way in which hues are perceived and savoured 

is stored within a given culture under the rubic of 

“traditional colours.” 

p. 5 

‘When we try to imagine colour, it may be necessary to 

erase from our minds all pre-established categories and 

return to a blank slate.’ 

‘But what if such parameters did not exist, and the 

words we had to describe colour were far fewer? Would 

we see colour the same way we do in today’s world?’ 

p. 6 

‘The names of colours function like a thread attached 

to a frightfully slender needle, capable of stitching 

together our most delicate emotions.’

On Signs Edited by Marshall Blonsky 

Essay: How Culture Conditions the Colours We See by Umberto Eco 

Reference: ECO, U., 1985. How culture conditions the colours we see. In: BLONSKY, M., ed. 1985. On signs. Oxford: Basil Blackwell, pp.157-175. 


Key Text 

Key Quotes 

p. 159 

Thus the puzzle we are faced with is neither a 

psychological nor an aesthetic one: it is a cultural one, 

and as such it is filtered through a linguistic system. We 

are dealing with verbal language in so far as it conveys 

notions about visual experiences, and we must, then, 

understand how verbal language makes the non-verbal 

experience recognizable, speakable and effable. 

p. 163 

This means that a given culture organizes the world 

according to given practices or practical purposes, and 

consequently considers as pertinent different aspects of 

the world. Pertinence is a function of our practices. 

p. 163 

Practices select pertinences 

p. 166 

Our discrimination ability for colours seems to be 

greater: we can detect the fact that hues gradually 

change in the continuum of a rainbow, though we 

have no means to categorise the borderlines between 

different colours. 

p. 167 

A trained artist can discriminate and name a great many 

hues, which the pigment industry supplies and indicates 

with numbers, to indicate an immense variety of colours 

easily discriminated in the industry. 

p. 168 

The largest collection of English colour names runs to 

3000 entries (Maerz and Paul), but only eight of these 

commonly occur. (Thorndike and Lorge). 

p. 168 

It seems that Russian speakers segment the range of 

wavelengths we call “blue” into different portions, 

goluboj and sinij. Hindus consider red and orange a 

unified pertinent unit. And against the 3000 hues that, 

according to David Katz, the Maori of New Zealand 

recognise and name by 3000 different terms , there are, 

according to Conklin, the Hanunoo of the Philippines, 

with a peculiar opposition between a public restricted 

code and more or less individual, elaborated ones. 

p. 171 

The different ways in which cultures make the 

continuum of colours pertinent, thereby categorising 

and identifying hues or chromatic units, correspond to 

different content systems. This semiotic phenomenon 

is not independent of perception and discrimination 

ability; it interacts with them and frequently overwhelms 

them. 

p. 171 

The names of colours, taken in themselves, have no 

precise chromatic content: they must be viewed within 

the general context of many interacting semiotic 

systems. 

p. 171 

We are animals who can discriminate colours but we are, 

above all, cultural animals. 

p. 174 

I do not think it is possible to found a system of 

communication on a subtle discrimination between 

colours too close to each other on the spectrum… we 

potentially have a great capacity for discrimination, 

and with ten million colours it would be interesting 

to compose a language more rich and powerful than 

the verbal one, based as it is upon no more than forty 

phonemes. 

p. 174 

The fact that a painter... can recognise and name more 

colours, the fact that verbal language itself is able 

not only to designate hundreds of nuances, but also

describe unheard-of tints by examples, periphrases and 

poetic ingenuity – all this represents a series of cases of 

elaborate codes. 

p. 174 

In everyday life, our reactivity to colour demonstrates a 

sort of inner and profound solidarity between semiotic 

systems. Just as language is determined by the way 

in which society sets up systems of values, things and 

ideas, so our chromatic perception is determined by 

language. 

Reference: ECO, U., 1985. How culture conditions the colours we see. In: BLONSKY, M., ed. 1985. On signs. Oxford: Basil Blackwell, pp.157-175. 


Key Text 

A Dictionary of Colour by Ian Paterson 

A L E X I C O N O F T H E L A N G U A G E O F C O L O U R 

A D I C T I O N A RY O F 

COLOUR 

I A N P A T E R S O N 

A 

Z Y X W V U T S R Q P O N M L K J I H G F E D C B A 

n aal 

A red dye from the plant of the same name related to the madder plant (and a 

useful word for word game players). 

n abaiser 

Ivory black. 

n abozzo, abbozzo 

An underpainting in one colour; a sketch. 

c absinthe, absinth 

The light green colour of the potent liqueur of the same name which was banned 

in France in 1915 because of its effect on health and the performance of French 

troops at the beginning of WW1. It continues to be banned in France and in the 

US but is allowed in the UK where it has been imported since 1998. The liqueur 

takes on a milky colour when water is added. 

c acacia 

A greyish or greenish yellow colour. 

c academy blue 

A mixture of viridian and ultramarine; a greenish blue. 

8 A D I C T I O N A R Y O F C O L O U R

Appendix two: 

The colours in alphabetical order 

Legend 

* indicates one 

of the 140 colours in 

the X11 Color Set 

b black 

bl blue 

br brown 

d dark 

g green 

gl gold 

gr grey 

g absinthe, absinth 

y acacia 

bl academy blue 

br acajou 

g acid green 

y acid yellow 

y acid-drop yellow 

met acier 

g Ackermann’s Green 

br acorn brown 

l light 

ll lilac, lavender 

met metallic colour 

mul multi-coloured or 

single coloured 

o orange 

p pink 

pl pearl, rainbow-like, 

iridescent 

pp purple 

y acridine yellow 

bl Adam blue 

p adobe 

r Adrianople red 

br adust 

g æruca 

v african violet 

bl Air Force blue 

bl air-blue 

r alesan 

r red 

s silver 

v violet 

w white 

y yellow 

T H E C O L O U R S 

455 

br burnt sugar caramel 

br burnt-almond 

br cacao brown 

br café 

br café au lait 

br camel 

br cannelas 

br canvas 

br caramel 

br Cassel brown 

br catechu brown 

br cedar 

br charcoal brown 

br chestnut 

br chestnut-brown 

br chocolate 

br Chocolate* 

br chocolate brown 

br cigar 

br clay 

br clove brown 

br cocoa 

br cocoa brown 

br coffee 

br cognac 

br columbine 

Reference: PATERSON, I., 2004. A dictionary of colour : a lexicon of the language of colour. London : Thorogood. 


br cork 

br Cornsilk* 

br cuir 

br DarkGoldenrod* 

br DarkKhaki* 

br dead-leaf brown 

br drab 

br dun 

br fallow 

br fawn 

br filemot 

br fox 

br foxy 

br French beige 

br French yellow 

br golden brown 

br grain 

br grège 

br Hatchett’s brown 

br havana 

br hazel 

br hickory 

br inca brown 

br Indian brown 

br khaki 

br Khaki* 

486 A D I C T I O N A R Y O F C O L O U R

Key Text 

Interaction of Colour by Josef Albers 

Reference: ALBERS, J., 2006. Interaction of colour. New Haven : Yale University Press.

Colour Plates from the Book – Examples of Colour Interaction 

Reference: ALBERS, J., 2006. Interaction of colour. New Haven : Yale University Press. 


Key Text 

Key Quotes 

p. 1 

In visual perception a color is almost never seen as it 

really is—as it physically is. This fact makes color the 

most relative medium in art. 

In order to use color effectively it is necessary to 

recognize that color deceives continually. 

First it should be learnt that one and the same color 

evokes innumerable readings. 

p. 2 

Practical exercises demonstrate through color deception 

(illusion) the relativity and instability of color. And 

experience teaches that in visual perception there is a 

discrepancy between physical fact and psychic effect. 

p. 3 

“If one says “Red” (the name of a color) and there are 50 

people listening, it can be expected that there will be 50 

reds in their minds. And one can be sure that all these 

reds will be very different. 

Even when a certain color is specified which all listener 

have seen innumerable times—such as the red of 

the Coca-Cola sign which is the same red al over the 

country—they will still think of many different reds. 

Even if all the listeners have hundreds of reds in front of 

them from which to choose the Coca-Cola red, they will 

again select quite different colors. And no one can be 

sure that he has found the precise red shade. 

And even if that round red Coca-Cola sign with the 

white name in the middle is actually shown so that 

everyone focuses on the same red, each will receive the 

same projection on his retina, but no one can be sure 

whether each has the same perception. 

When we consider further the associations and reactions 

which are experienced in connection with the color and 

Reference: ALBERS, J., 2006. Interaction of colour. New Haven : Yale University Press. 

the name, probably everyone will diverge again in many 

different directions. 

What does this show? 

First, it is hard, if not impossible, to remember distinct 

colors. This underscores that important fact that the 

visual memory is very poor in comparison with our 

auditory memory. Often the latter is able to repeat a 

melody heard only once or twice. 

Second, the nomenclature of color is most inadequate. 

Though there are innumerable colors—shades and 

tones—in daily vocabulary, there are only about 30 

names.” 

p. 45 

Usually, we think of an apple as being red. This is not 

the same red as that of a cherry or tomato. A lemon is 

yellow and an orange is like its name. Bricks vary from 

beige to yellow to orange, and from ochre to brown to 

deep violet. Foliage appears in innumerable shades of 

green. In all these cases the colors named are surface 

colors.

Key Text 

The Elements of Colour by Johannes Itten 

Reference: ITTEN, J., 1970. The Elements of Colour. London : Chapman and Hall.

Examples of Different Types of Contrast from The Elements of Colour 

Reference: ITTEN, J., 1970. The Elements of Colour. London : Chapman and Hall. pp. 35, 39, 47 & 51 


Key Texts 

Basic Colour Terms, Their Universality and Evolution by Brent Berlin and Paul Kay 

Reference: BERLIN, B. & KAY, P., 1999. Basic colour terms: their universaility and evolution. Stanford : Center for the Study of Language and Information.

Colour for Philosophers by C. L. Hardin 

Reference: HARDIN, C. L., 1988. Colour for philosophers. Indianapolis : Hackett Publishing Company. 


Other Key Texts 

Colour Codes by Charles A. Riley Colour Edited by David Batchelor 

Reference: RILEY, C. A., 1995. Colour Codes: modern theories of colour in philosophy, painting and architecture, literature, music and psychology. London; Hanover: University 

Press of New England. Reference: BATCHELOR, D. ed. 2008. Colour. Cambridge, Mass. : MIT Press.

Bright Earth by Philip Ball Colour by Victoria Finlay 

Reference: BALL, P., 2008. Bright Earth. London : Vintage. 

Reference: FINLAY, V., 2003. Colour: Travels Through the Paintbox. London : Sceptre. 


Key Text 

A Colour Alphabet and the Limits of Colour Coding by Paul Green-Armytage 

Summary 

This paper describes a series of studies designed to 

investigate the possible limits to the number of different 

colours that can be used in a colour code and the 

relative merits of colours and shapes for communicating 

information. The studies took their particular form in 

response to an observation by Rudolf Arnheim that 

an alphabet of 26 colours would be unusable. It was 

found that a text, with letters represented by coloured 

rectangles, can be read, first with the help of a key and 

then without. The colour alphabet, tested in competition 

with other alphabets made up of unfamiliar shapes 

and faces, was read more quickly than the others. 

Speed of reading was only matched with an alphabet 

made up of shapes that were familiar and nameable. 

Colours are most helpful for quick identification and 

for clarifying complex information, but where more 

than 26 distinctions must be made colours must be 

supplemented by shapes, typically in the form of letters 

and numbers. 

Introduction 

This paper is an elaboration, with some new material, 

of the paper presented at the 11th Congress of the 

International Colour Association (AIC) in Sydney, 

Australia [1]. The paper reflects an on-going interest in 

problems of colour coding and the ways in which colours 

and shapes can be used for communicating information. 

The main focus of the paper is on ways to determine the 

maximum number of different colours that can be used 

in a colour code without risk of confusion. 

The number of different colours that can be used in 

a colour code will be greater for people with normal 

colour vision than for those without. While some 

reference will be made to the limitations experienced by 

people with defective colour vision, the discussion will 

be concerned mainly with problems of colour coding for 

people with normal colour vision. 

In the first section, some of the problems associated 

with colour coding are illustrated by the colours used to 

identify the different routes on transport maps. There 

are different approaches to the problem of selecting 

colour sets for colour codes. One approach is to work 

within a chosen colour space and take a series of 

points within that space as far apart from each other as 

possible. Another approach is to use colour naming as 

a means of generating a suitable range of colours. A 

benchmark for colour coding is the set of 22 colours of 

maximum contrast proposed by Kenneth Kelly in 1965 

[2]. 

Next, there is an account of the series of studies 

that were conducted to investigate the relative ease 

with which a text can be read when the letters are 

represented by colours or by unfamiliar shapes. A key 

to the colours and shapes was provided. The studies 

took their particular form as a response to a claim by 

Rudolf Arnheim that an alphabet of 26 colours rather 

than shapes would be unusable [3]. It turned out that 

letters can be represented by colours and combined in 

a text that can be read. A surprise finding was that the 

colours were read more quickly than the shapes. The 

studies were also concerned with the palette of colours 

that should be used and the way that colours should be 

assigned to letters. 

The findings from these studies led to a further study, 

described in the third section, to test the influence of 

simultaneous contrast on the ease with which colours 

can be identified. Simultaneous contrast comes into play 

on geological maps where the appearance of colours is 

affected by surrounding colours. Correct identification 

of colours from the key is more difficult as a result. The 

findings from this study led to a modification of the 

palette of colours used for the colour alphabet and 

to re-assignment of colours to letters. This modified 

alphabet was learned and a series of short poems were 

read without reference to a key. Reading time improved 

with practice but one or two mistakes were made 

with each poem. This suggests that 26 colours can be

taken as a provisional limit to the number of different 

colours that can be used in a code. The suitability of the 

alphabet colours for colour coding is supported by their 

striking similarity to Kelly’s colours of maximum contrast. 

The studies revealed the importance of simplicity and 

contrast where objects need to be identified quickly and 

easily. Provided the number of colours does not exceed 

26, colours can be identified more quickly than shapes. 

Shapes also need to be simple and very different from 

each other if they are to be identified quickly. And 

there were two other factors, revealed by the studies, 

that contribute to speed and ease of identification. 

Shapes can be identified more quickly if they are familiar 

and can be named. Colours are already familiar and 

identification of colours is also made easier if the colours 

can be named. 

The relative strengths and weaknesses of colours and 

shapes for communicating information are evident 

on geological maps. Without colour the maps would 

be almost impossible to read but colours alone are 

not enough. The colour patterns reveal the broad 

distribution of the rocks, but there are more than 26 

kinds of rock to be identified. Slightly different colours 

may be used but the difference is too subtle. In order to 

establish the identity of every kind of rock each colour 

area is also marked by a letter-number code. Colours 

give quick access to the big picture; for the fine detail 

reliance must be placed on shapes. 

Colour Sets for Colour Coding 

The colours used to identify the different routes on 

transport maps are a familiar example of colour coding. 

Transport Map Problem 

What is the largest number of different colours that can 

be used to identify the different routes on a transport 

map without risk of confusion? Colour coding of 

different routes in a system of public transport can be 

very helpful. Consider this scenario: a traveller, arriving 


at Gothenburg Central Station in Sweden, has to meet a 

friend in suburban Kålltorp. The traveller asks how to get 

to Kålltorp and is told, ‘Take tram no.3, going east, to 

the end of the line. It is the blue route – the vivid blue, 

not the light blue which is route no.9.’ The Gothenburg 

trams have their route numbers and destinations shown 

on coloured panels above the drivers’ front windows. 

The colour on an approaching tram can be identified 

well before it is possible to read the number or the 

name of the tram’s destination. The same colours are 

used for the tram routes as shown on the Gothenburg 

transport map. Not only do the different colours identify 

the different routes, they also make the map easier to 

read. 

The task of selecting colours for identifying the different 

routes of the Gothenburg trams was described by 

Lars Sivik during the 1983 meeting of the International 

Colour Association [4]. Sivik’s account of that task led to 

consideration of the criteria that should be used when 

choosing colours for coding purposes. It also led to 

speculation about the limits, in terms of the number of 

different colours used in a coding system, beyond which 

colour coding would break down. 

The 1995 edition of the Gothenburg transport map 

shows nine tram routes [5]. The coloured route lines are 

presented on a grey background. The colours can be 

named: white, yellow, vivid blue, green, red, orange, 

brown, purple and light blue. 

Since 1995, the tram routes have been further modified. 

Two new routes are shown on the map that is available 

online [6] and further expansion of the system is 

planned. The new route 10 is identified by yellow– 

green and route 11 by black. Colour naming could be 

used as a means of extending the colour code. Pink 

could be used for a future route 12. Light green and 

light purple are distinct from vivid green and vivid 

purple and could be added for future routes 13 and 

14. Blue–green could be added for route 15. To make 

room for even further expansion it would be possible

Key Text 

to make slight modifications to the identifying colours 

of established routes. Orange, brown and purple could 

each be split into two separate colours. Existing routes 

6, 7 and 8 could now be yellow–orange, yellow–brown 

and red–purple which would allow for new routes 16, 17 

and 18 to be identified by red–orange, red–brown and 

blue–purple. The past, current and possible future route 

colours for the Gothenburg trams are shown on the left 

of Figure 1 as the ‘Gothenburg Palette’. 

Next to the Gothenburg Palette are the identifying 

colours used for other transport systems which have 

several established routes. The orders of the colours 

have been rearranged for easier comparison. The 

colours were matched visually to those on printed maps 

for the Tokyo Subway [7], the Paris Metro and RER [8,9], 

the London and the South East Rail Service [10] the 

London Underground [11] and the Oyster rail services in 

London [12]. The Paris RER routes are express services 

to the airports and outlying towns and are represented 

on the map by broader lines than those for the Metro. 

Travellers can transfer between the RER and the Metro. 

In London, the new Oyster card will allow travellers to 

transfer between the London Underground and mainline 

routes. The Underground routes are represented 

on the map by single lines, the mainline routes by 

double lines. The mainline routes are identified by the 

terminus stations which they serve and are colour coded 

accordingly. 

The comparison in Figure 1 shows how the Tokyo route 

colours could be more clearly differentiated. Three 

routes are identified by similar reds which could be 

confused. Two of these could be modified to match the 

red–orange and red–purple of the Gothenburg Palette. 

Two blues that are similar are used on the Paris map 

for RER route B and Metro route 13. These could also 

be made more distinct if one were made a lighter blue, 

but the potential confusion is avoided because they are 

differentiated by shape – the route lines are shown in 

different widths. Shape differentiation also overcomes 

several potential confusions between the colours used 

for the routes on the London Oyster map where single 

lines are used for the London Underground routes and 

double lines for the routes serving the mainline termini. 

It might be possible to find alternative colours for the 

London termini so that shape differentiation were no 

longer necessary on the London Oyster map and all 

24 routes were clearly differentiated by colour alone. 

The Paris Metro/RER system has some colours (for 

routes 3, 12 and 14) that have no clear equivalent 

in the Gothenburg Palette but which are still easily 

differentiated. This points to ways in which the range 

of colours could be extended in a solution to the 

transport map problem which might then be applied for 

London. A usable colour code with 24 colours might be 

possible. However, if the planners of the Oyster system 

had decided to identify the mainline routes as they are 

on the London and the South East Rail Services map 

they would have needed 19 colours for the mainline 

routes to be combined with the 13 well established 

route colours of the London Underground. Several of 

the Underground colours have confusable equivalents 

on the London and the South East Rail Services map as 

can be seen in Figure 1. A range of 32 colours would be 

needed. It seems unlikely that a solution to the transport 

map problem would be such a large number. 

Colours of maximum contrast 

Identifying the different routes on a transport map is 

one of many possible applications for a colour code. 

In a more general discussion of colour coding Robert 

Carter and Ellen Carter discuss problems of choosing 

colour sets that will be most effective for communicating 

information in a given situation [13]. They also pose the 

question, ‘What is the maximum number of colours that 

can be used?’ 

In response to requests for sets of colours that would 

be as different from each other as possible for purposes 

of colour coding, Kenneth Kelly proposed a sequence 

of colours from which it would be possible to select 

up to 22 colours of maximum contrast [2]. Kelly made

use of the Inter-Society Color Council and National 

Bureau of Standards (ISCC-NBS) method of designating 

colours [14] and selected his colours from the ISCC- 

NBS Centroid Color Charts [15]. The colours are listed 

in a table together with general colour names, their 

ISCC-NBS Centroid numbers, their ISCC-NBS colour 

name abbreviations and Munsell notations. Kelly’s list, 

with colour samples matched visually to the ISCC-NBS 

centroid colours, is shown in Figure 2. 

The order of colours in Kelly’s list was planned so that 

there would be maximum contrast between colours 

in a set if the required number of colours were always 

selected in order from the top. So a set of five colours 

should be white, black, yellow, purple and orange. And 

if seven colours were required, light blue and red should 

be added. Kelly took care of the needs of people with 

defective colour vision. The first nine colours would be 

maximally different for such people as well as for people 

with normal vision. These nine colours are also readily 

distinguishable by colour name. The dotted line in 

Figure 2 separates these from the other colours on the 

list. 

Carter and Carter [13] make reference to Kelly’s work 

and verify his assumption that the ease with which two 

colours can be discriminated depends on how far apart 

the colours are in colour space. From the colour spaces 

available at the time they chose CIE L*u*v* as most 

appropriate for their study. They recognised that the key 

to their problem was to establish the smallest degree 

of difference between two colours that would still allow 

people to discriminate the colours with acceptable 

ease. They found that people’s ability to identify colours 

correctly diminished rapidly when the distance between 

colours was less than 40 CIE L*u*v* units. They provide a 

rough answer to their own question about the maximum 

number of usable colours: their Table 1 shows that 

colours in a set of 25 could all be separated by at least 

51.6 CIE L*u*v* units. 


In a later study, Carter and Carter investigated the role 

of colour coding for rapid location of small symbols on 

electronic displays [16]. They show how ease and speed 

of location are influenced, in part, by the degree of 

difference between colours, but also by the size and 

luminance of the symbols in relation to the surround. 

In their earlier study [13], Carter and Carter propose an 

algorithm for establishing colour sets within CIE L*u*v* 

space. Building on the work of Carter and Carter, others 

have proposed algorithms for generating colour sets 

[17,18]. The ISCC set up Project Committee 54 with 

the intention of bringing Kelly’s work up to date [19]. 

However, the committee decided that, for what they 

were trying to do, they could not improve on Kelly’s set 

of colours [20]. Robert Carter and Rafael Huertas have 

investigated the use of other colour spaces and colour 

difference metrics for generating colour sets [21]. They 

also refer to an alternative approach, investigated by 

Smallman and Boynton, whereby a colour code could be 

based on colour name concepts. 

Colour Naming and Basic Colour Terms 

The concept of ‘basic colour terms’ was introduced by 

Brent Berlin and Paul Kay in their landmark study which 

was published in 1969 [22]. Berlin and Kay mapped the 

basic terms of 20 languages on an array of 329 colours 

from the Munsell colour order system. They claim 

that ‘a total universal inventory of exactly eleven basic 

colour categories exists from which the eleven or fewer 

basic colour terms of any given language are always 

drawn.’ They list the basic colour terms for English as: 

white, black, red, green, yellow, blue, brown, purple, 

pink, orange and grey. Participants in their study 

had indicated the range of colours which they would 

describe by each name and also pinpointed the best, 

most typical example of each. 

Some colour names are mapped onto a much larger 

range of different colours than other colour names. This 

means that it is possible to make additional distinctions 

such as that between light and vivid blue as for the 

Gothenburg tram colours. Further distinctions can be

Key Text 

made by using composite names such as yellow–green 

and blue–green. While the difference in appearance 

between the colours may be the key to a successful 

colour code, the naming structure, as mapped by Berlin 

and Kay, could be used as a starting point. This was 

the approach used for the Gothenburg Palette and it is 

surely an advantage if the colours in a code can also be 

named. This is clear from the example given above of a 

traveller arriving in Gothenburg and needing to get to 

Kålltorp. 

Relating Colour Names to Colour Space 

The number of colours that can be named by the ISCC- 

NBS method of designating colours, as used by Kelly, is 

267. This is level three of the ‘Universal Color Language’ 

(UCL), with its six levels of increasing precision. The UCL 

is published by the US Department of Commerce [14]. 

Munsell colour space [23] is subdivided into smaller 

and smaller blocks, each block containing a range 

of colours that are identified by the same name. The 

ISCC-NBS centroid colours represent the focal colours 

for the 267 blocks at level three. At level one, with 13 

colours, the blocks are much larger and the naming 

of the range of colours within each block is much less 

precise. There are 29 colours at level two. At level four 

are the thousand or more colours in a colour order 

system such as Munsell. Interpolation between colour 

standards, and then the use of measuring instruments, 

increases the number of colours to about 500 000 at 

level five and 5000 000 at level six. A Munsell notation 

is provided for each colour in the ISCC-NBS Centroid 

Color Charts. The focal colours for levels one and two of 

the UCL, matched visually to the designated ISCC-NBS 

centroid colours, are shown in Figure 3. The level one 

colours are represented by circles, the colours added 

at level two are represented by diamonds. The colours 

are arranged approximately according to their Munsell 

hues and lightness values on the gird used by Berlin and 

Kay to record the way that colour names were mapped. 

The shaded areas in Figure 3 represent the range of 

colours that would be described by each colour name as 

recorded by English speaking participants in the Berlin 

and Kay study: white, grey, black, pink, red, orange, 

brown, yellow, green, blue and purple. 

The 29 colours at level two of the UCL could be 

considered as a basis for a colour code. However, 

some of the colours might be too similar for confident 

identification and there are also areas of colour space 

that are not well represented. 

A simpler alternative to the first three levels of the UCL 

is the three-level system of Colour Zones [24,25]. The 

structural framework for the Zones is that of the Natural 

Color System (NCS) [26]. The reference points for the 

NCS, and for the Colour Zones, are the Elementary 

Colours (ürfarben) proposed by Ewald Hering: Yellow, 

Red, Blue, Green, White and Black [27]. These are 

not physical samples but ideas such as a yellow that 

is neither reddish, greenish, blackish nor whitish. The 

appearance of any colour can be described in terms of 

its relative resemblance to these conceptual reference 

points. So the ISCC-NBS centroid colour ‘Vivid Yellow 

Green’ would be described as 50% yellowish, 50% 

greenish, 10% whitish and 10% blackish. Colour Zones 

are subdivisions of the NCS colour space. Each zone 

contains a range of similar colours with a focal colour 

as a reference point at the centre of the zone. Hering’s 

Elementary Colours are the focal points for the six zones 

at level one. Further subdivisions provide 27 zones at 

level two and 165 zones at level three. 

The colours from levels one and two of the Colour Zones 

system are shown in Figure 4. The Elementary Colours, 

at level one, are represented by circles and the colours 

added at level two by diamonds. The colour names, 

selected after extensive research, should be generally 

acceptable and can be defended. The symbols below 

each column of colours indicate the hue zone to which 

the colours belong. The symbols to the right of each row 

of colours indicate the nuance zone. 

The 27 colours at level two of the Colour Zones system 

could also be used as a basis for a colour code. They

were tested as part of the colour alphabet project which 

is described in the next section. 

Colour Alphabet Project 

A palette of colours that represented a solution to the 

transport map problem would be of practical value for 

a number of situations which call for colour coding. This 

was one of the motivating considerations for a workshop 

which was conducted for members of the Colour Society 

of Australia in 2007. The workshop followed a morning 

of lectures on the topic ‘colour as information’. The plan 

of the workshop was to investigate the transport map 

problem and also to test the relative merits of colours 

and shapes for communicating information. The activity 

took its particular form as a response to an observation 

by Rudolf Arnheim. In his book, Art and Visual 

Perception [3], Arnheim compares shape and colour for 

their power of discrimination: ‘ 

… we acknowledge that shape lets us distinguish an 

almost infinite number of different individual objects. 

This is especially true for the thousands of human 

faces we can identify with considerable certainty on 

the basis of minute differences in shape. By objective 

measurement we can identify the fingerprints of one 

specific person among millions of others. But if we 

tried to construct an alphabet of 26 colours rather than 

shapes, we would find the system unusable. … we are 

quite sensitive in distinguishing subtly different shades 

from one another, but when it comes to identifying a 

particular colour by memory or at some spatial distance 

from another, our power of discrimination is severely 

limited.’ 

In this argument in favour of shape, Arnheim could be 

accused of ‘moving the goal posts’; shape and colour 

need to be compared on equal terms. If ‘objective 

measurement’ is allowed, the number of colours that 

could be discriminated by a spectrophotometer would 

also be in the millions as they are at level six of the UCL. 

But if things are to be identified ‘by memory or at some 


spatial distance’ the number would fall dramatically, 

whether it were colours, faces or fingerprints. 

Rudolf Arnheim challenge 

Participants in the workshop took up the ‘Rudolf 

Arnheim challenge’. The aim was to see if it is possible 

to construct a usable alphabet of 26 colours. While it 

could not be claimed that a usable colour alphabet 

would represent a definitive answer to the transport 

map problem, such a palette of 26 colours could show 

how the Gothenburg Palette could be extended. It 

could also provide colours for identifying the mainline 

routes on the London Oyster map which would be 

distinct from the Underground route colours. 

There were 28 participants in the workshop and two 

exercises. Participants were first given a sheet on which 

a palette of 76 colours had been printed in a square grid 

and a sheet with blank boxes next to the letters of the 

alphabet. The task for the participants was to choose, 

from this palette, a colour for each letter. They were to 

cut out coloured squares for the letters, and stick them 

down in the appropriate boxes. 

The second task was to translate a poem which had 

been rendered in one of three new ‘alphabets’, which 

had been prepared before the workshop. One alphabet 

was made of colours, one of unfamiliar shapes, and the 

third of unfamiliar faces. In later studies, further tests 

were carried out with alphabets made up of different 

colours, shapes and faces. 

Strategies for choosing colours for an alphabet 

When the participants had completed the exercises, 

the colour alphabets they had proposed were displayed 

and the various strategies for selection were discussed. 

Some participants started with the colours and selected 

26 that made a satisfying visual sequence. The colour 

order came first and the letters followed. However, most 

participants worked in the opposite direction. While it 

may be desirable that a colour alphabet be beautiful, 

it is more important that it be legible. There were two

Key Text 

aspects to the task: what colours should be selected 

and how should the colours be assigned to particular 

letters. 

For several participants, a key requirement was that 

the colours be as different from each other as possible. 

Colours that are maximally different from each other 

are most helpful in systems of coding. Participants in 

the workshop were not in a position to apply the kind 

of method used by Carter and Carter [13] and had to 

rely on their own judgements when choosing, from the 

printed squares, those colours that they considered 

to be maximally different from one another. One 

participant, Tony Marrion, made a point of sticking 

down colours next to the selected colours with which 

they might be confused. 

There were many approaches to the problem of 

assigning colours to letters. Some participants 

looked for ways of grouping the letters as a basis for 

linking them to groups of colours. Many participants 

considered ways of distinguishing vowels and 

consonants such as using achromatic colours for the 

vowels. The colour choices were studied to see if there 

were any cases where a significant number of people 

had chosen the same colours for the same letters. The 

colours chosen for the letters are shown in Figure 5. 

The association of particular colours with particular 

letters is known to be an experience of people 

with synaesthesia. Such people have cross-sensory 

experiences; they might ‘taste’ sounds or ‘hear’ smells. 

The experience of synaesthesia might be a good 

basis for the choice of colours for an alphabet if the 

associations of colours with letters were consistent for all 

letters, from person to person, but this does not always 

seem to be the case [28,29]. However, there is data to 

support some colour-letter connections. In a large-scale 

study of synaesthesia, Rich et al. showed how people 

with synaesthesia, and people without, link the eleven 

basic colour terms with letters of the alphabet [30]. 

People with and without synaesthesia share some strong 

associations: I is white; X is grey or black; Z is black; 

A and R are red; G is green; Y is yellow; B is blue; D is 

brown; V is purple; and P is pink. There are no strong 

associations shared by the two groups for orange. For 

those with synaesthesia it is J, for those without it is O. 

Colour Names 

A striking feature of the data collected by Rich et al. is 

the link between colours and the initial letters of colour 

names, i.e. B for blue, R for red, etc. These colour– 

letter associations also feature in the choices made 

by participants in the workshop and there were other 

colour name associations: several chose lime green for 

L and turquoise for T. Some participants wrote down 

their colour names; one used the names of fruit and 

vegetables – apple, banana, carrot, etc. – and another, 

Joan Hodsdon, managed to find a name for every letter. 

Being able to name the colours would surely be an 

advantage where colours need to be distinguished from 

one another and remembered. M D Vernon points out 

that ‘One of the obstacles to remembering colours, 

especially the intermediate shades, is the paucity of 

generally accepted colour names’ [31], and Ammon 

Shea argues for the value of a large vocabulary. Shea is 

a collector of words and has completed the eccentric 

task of reading all 20 volumes of the Oxford English 

Dictionary. He explains how his knowledge of words has 

increased his sensitivity to his surroundings, ‘If I know 

there is a word for something…I will stop and pay more 

attention to it’ [32]. 

Many of these strategies were used for the prototype 

alphabet which had been designed before the 

workshop: colours of maximum contrast; vowels 

separated from consonants by colour nuance; colours 

linked to names, etc. Pale colours were used for vowels 

so that words might have clearer colour patterns, much 

as the heraldic ‘rule of tincture’ leads to highly legible 

designs by ensuring that there is strong contrast of 

lightness values between elements [33]. In a coat of 

arms, a shape – such as a cross – can be a colour (red, 

blue, black, green, purple) or a metal (silver/white, gold/ 

yellow) but the background must be from the other

group. Colour can never be used on colour or metal on 

metal. In practice it turned out that the juxtapositions of 

letters in words is too varied for that particular strategy 

to be helpful. 

Testing alphabets made of colours, unfamiliar shapes 

and unfamiliar faces 

For the second task at the workshop, the participants 

were given a poem which had been rendered in one of 

three new ‘alphabets’. The poem was a nonsense poem 

containing all the letters of the alphabet. It was provided 

with a key to the alphabet and a sheet with blank spaces 

for writing down the words of the poem. For each 

alphabet there was a prize for the person who wrote 

out the poem first. The poem is shown in each of the 

alphabets, together with the key, in Figures 6, 7 and 8. 

The unfamiliar shapes used for the alphabet in Figure 7 

are the ‘capital letters’ for ‘Dingbats’ which are available 

as a font on many computers. The faces for the alphabet 

in Figure 8 were isolated from a group photograph of 

staff and students at the Western Australian Institute of 

Technology taken in 1973. 

This is the poem: 

‘THE YUGOSLAV 

IF ORTHODOX 

KEEPS HIS PYJAMAS 

IN A BOX 

AND FREQUENTLY 

BESTOWS A PRIZE 

ON COWS WITH 

SUPERCILIOUS EYES’. 

The participants at the workshop found it quite easy 

to read the poem printed in colours (Figure 6). So 

there was an answer for Arnheim – it does seem to be 

possible to construct an alphabet of 26 colours that 

is usable, if only in this limited sense. But there was a 

surprise: the colours were read more quickly than the 

shapes or the faces. All those who had been given the 

poem printed in colours had completed the task before 

any of those who were trying to read the faces. 


During the discussion, people complained that the 

shapes (the Dingbats) (Figure 7) were too similar and 

that the faces were too small (Figure 8). So the next 

step was to find out whether these results were due to 

a natural superiority of colours for this kind of task or to 

the choice of those particular shapes and faces which 

might have given the colours an advantage. There was 

an opportunity for follow-up studies with two groups 

of first year students of Design at Curtin University of 

Technology. It was possible to test new alphabets of 

colours, shapes and faces. 

Alphabets for follow-up studies 

It was a striking coincidence that there are 27 colours 

in the Colour Zones system at level two, one of which 

is white. With 26 colours for the 26 letters on a white 

background, it was possible to test the potential 

usefulness of the Colour Zones palette for a colour 

code. The follow-up studies also made it possible to 

see if an arbitrary assignment of colours to letters would 

make any difference to ease of legibility. The colour 

alphabet used for the follow-up studies is shown in 

Figure 9. 

For the follow-up studies four new sentences were 

composed, each containing all the letters of the 

alphabet. This made it possible for each student to 

try reading all three kinds of alphabet and it would be 

possible to compare an individual’s performance in 

each task. The new sentences, with 61 or 62 letters, 

were shorter than the poem used at the workshop. 

This meant that the faces could be larger on the sheet 

of A4 paper. For the first group of students it was also 

decided to use a real but unfamiliar alphabet instead of 

the Dingbats. The Georgians, in Eastern Europe, use an 

alphabet that is quite unlike the more familiar Greek or 

Cyrillic alphabets. 

Georgian fonts can be downloaded from the Internet. 

There were 43 students in the first group. The results 

were similar to those from the workshop, with colours 

being read most quickly. The potential usefulness of the

Key Text 

Colour Zones palette for colour coding was supported 

by this result. It also appeared that, in this situation 

where reference could be made to a key, the arbitrary 

assignment of colours to letters had not affected ease of 

legibility. 

A more accurate record was kept of the times taken to 

read the sentence and write it down. The average times 

were: colours, 3 minutes 8 seconds; shapes (Georgian), 

4 min 50 s; faces, 5 min 30 s. When these times were 

discussed, it was suggested that familiarity might have 

been a factor. It was pointed out that, while the shapes 

and faces were new to people, ‘they already knew the 

colours’. 

The project took on a new dimension: a challenge to 

create alphabets of shapes or faces that could be read 

as quickly as the colours. For the second group, of 55 

students, two new alphabets were created. Movie stars 

were chosen for the alphabet of faces. Their faces are 

familiar and available for downloading from the Internet. 

A survey was conducted to find out which movie 

stars the students would recognise most easily. Two 

alphabets of movie stars were used. For one, the movie 

stars were assigned to letters which corresponded with 

the first letters of their surnames (Rowan Atkinson, Halle 

Berry, Tom Cruise, etc.). For the other, the most familiar 

movie stars were assigned to letters in an arbitrary way. 

(Copyright prevents publication of these alphabets.) 

For the shapes, reference was made to the results of 

an old study to find nameable shapes. This time it was 

also important for the shapes to be familiar; 26 shapes 

were needed that were simple and familiar with names 

beginning with each letter of the alphabet – arrow, 

bottle, cross, etc. 

One of the new sentences, rendered in four different 

alphabets, is shown in Figure 10. The original alphabet 

of faces was used for the first group, but with the faces 

larger and separated by small gaps. The sentence 

is shown in Georgian at bottom left and in the new 

alphabet of shapes at bottom right. 

This is the sentence: 

‘WE NEVER 

EXPECTED 

TO QUIT 

SMOKING 

JUST BECAUSE 

OF A HOLE 

IN THE 

OZONE LAYER’. 

With the second group, the familiarity of shapes and 

faces did make a difference. The average time for 

reading the colours was similar to that for the first group 

but the shapes and faces were read much more quickly. 

The average times were: colours, 3 min 20 s (twelve 

seconds slower than the first group); shapes, 2 min 55 s 

(nearly two minutes quicker than the first group); faces, 

4 min 39 s (nearly one minute quicker than the first 

group). In spite of the familiarity of the movie stars, that 

alphabet of faces still took significantly longer to read. 

One student made the telling comment, ‘Too much 

information’. 

With the new alphabet of shapes, it was finally possible 

to match the colours. The shapes were simple, clearly 

differentiated and familiar. Some students also realised 

that the initial letters of the shapes’ names were the 

corresponding letters in the sentence. Since most 

shapes were easily named it was not necessary to spend 

time checking the key. One student took just 61 seconds 

to read the sentence. The correspondence between 

letters for the alphabet and the first letters of the 

movie-stars’ surnames did not seem to help, perhaps 

because the connection was not immediately obvious. 

Nevertheless, the value of nameability was established 

with the shapes and this could be extended to colours. 

An alphabet of colours that had names, such as those 

proposed by some participants at the workshop, should 

be quicker to learn and easier to read. 

The conclusion from the workshop and follow-up 

studies, therefore, was that there are four factors that 

contribute to quicker and easier reading: simplicity, 

contrast, familiarity and nameability.

Further Tests and Modifications to the Colour 

Alphabet 

It was evident from the workshop and follow-up studies 

that the Colour Zones palette can be used to represent 

letters of the alphabet and that these colours can be 

put together to form words and sentences that can be 

read. However, there was no certainty that this was the 

optimum palette. It is an advantage to be able to name 

the colours but some colours in the palette appear 

quite similar. With colours juxtaposed to form words it 

seemed possible that the appearance of colours could 

be affected by the influence of neighbouring colours 

to such an extent that a colour forming part of a word 

might be mistaken for another colour from the palette 

with the result that the word would be misinterpreted. 

Geological Map Problem 

Colour coding is stretched beyond its limit on 

geological maps. The geology of an area can be very 

complex. To record the varied age and nature of the 

rocks, the Geological Survey of Western Australia 

required 97 different ways to mark the map [34]. These 

are shown in the key. Different colours are used but in 

some cases a textured pattern is superimposed on the 

colour and in all cases there is also an identifying letter– 

number code. 

Without colour it would be extremely difficult to follow 

the intricate contours which separate areas of different 

rock, such as dolerite and granite. But there are five 

different greens for dolerite and five different pinks or 

pinkish reds for granite. To identify rocks of different 

age the greens and pinks are marked d1, d2, g1, g2, 

etc. Without this letter–number code it would not be 

possible to check from the map to the key and be 

confident that a particular area of pink represented 

granite that was over 3 billion years old (g1) or granite 

that was just over 2 billion years old (g3). The situation 

is aggravated by the phenomenon of simultaneous 

contrast: the g3 pink for 2 billion-year-old granite, 

when surrounded by the blue for siltstone, looks 


different from that g3 pink surrounded by the yellow for 

limestone and different again when surrounded by the 

white background of the key. A detail of the geological 

map of Western Australia, and part of the key, are shown 

in Figure 11. 

The geological map problem is more challenging than 

the transport map problem. On a transport map the 

background colour is generally uniform. On a geological 

map the background colours are constantly changing. 

Simultaneous contrast comes into play so that areas 

printed with the same ink formula (in that sense being 

the ‘same colour’) can appear different on different 

areas of the map and different again against the white 

background of the key. 

Testing the colour zones palette as a potential colour 

key for a geological map 

A new study was carried out, with 12 participants, to 

test the colours of the Colour Zones palette in a context 

that was similar to that of a geological map. There were 

40 different colours, which included the Colour Zones 

colours, assigned by a random process to the squares in 

a 5 × 8 grid. The colours were given names as for girls 

and boys: Anne, Brad, Cora, Dick, etc. The names were 

put in a bowl and drawn out, one after the other, to 

determine the colours of the squares. Small rectangles 

of these colours were arranged, with their identifying 

names, as a key beside the grid. On each coloured 

square there was a figure – a letter, number or other 

symbol – in one of the Colour Zones colours. The same 

random process of drawing names out of a bowl was 

used to determine the colours of the figures. Since there 

were 40 squares but only 26 colours in the Colour Zones 

palette (excluding white) this meant that some colours 

were used for more than one figure. If the process led 

to the colour for the figure being the same as that for 

the square background a new name was drawn from the 

bowl. The task for the participants was to identify the 

colours of the figures and the square backgrounds by 

referring to the key. Three sheets were prepared which 

had the same sequence of background colours but

Key Text 

different colours for the figures. One of these sheets is 

shown in Figure 12. The colours marked with a star in 

the key are the colours from the Colour Zones palette. 

Most people were able to identify the colours of the 

background squares. Eight of the twelve participants 

identified all the background colours correctly. Where 

confusions occurred, the colours involved were of the 

same or immediately neighbouring hues. The white 

background of the colours in the key makes them 

appear slightly darker than the ‘same colours’ in the 

squares, so Dora as the colour of the background to K 

could be confused with Dick in the key and Owen as the 

background to Q could be confused with Tara in the key. 

Where colours of neighbouring hues were confused they 

were of the same nuance and, typically, in the range 

of hues from blue to green. So, Faye was confused 

with Greg and Nora with Owen. These results can be 

seen in relation to the law of simultaneous contrast as 

formulated by M E Chevreul, ‘In the case where the eye 

sees at the same time two contiguous colours, they will 

appear as dissimilar as possible, both in their optical 

composition and in the height of their tone’ [35]. 

The influence of simultaneous contrast was much 

stronger for the figures. The participants studied all 

three sheets for a total of 120 coloured figures. The 

average number of errors was 13. Two participants 

identified all but three colours correctly; the least 

successful participant made 31 errors. In most cases the 

errors could be predicted from the laws of simultaneous 

contrast. Fred is the colour of the letter D, but four 

people saw it as Emma and one as Evan. In each case 

the lime green surround makes the D appear more 

bluish. Ivan is the colour of the letter G but four people 

saw it as Adam. The dark brown surround makes the 

G appear lighter. The letter X, which is Gina, is almost 

invisible against its background. The background colour, 

Fred, is more bluish and makes the X appear more 

yellowish. Ten people saw the X as Hugo. In a later 

section of his book, Chevreul explains how a grey figure, 

surrounded by a given colour, will appear tinged with 

the complementary of that colour. The colour of the 

number 9 is Sara but three people saw it as Dora. 

Modifications to the colours for the alphabet 

This study showed that the colours in the Colour 

Zones palette (see Figure 4) that were most commonly 

confused were those in the range of hues between Blue 

and Green. The study also showed that vivid colours are 

more easily identified than light or deep colours. The 

percentage averages for correct identification were: 

93.5% for vivid colours, 90.3% for light colours and 

85.0% for deep colours. This implies that the legibility 

of the colour alphabet could be improved by departing 

from strict adherence to the Colour Zones structure, 

with its eight focal colours in each of the vivid, light and 

deep nuance zones. 

The Colour Zones palette can be compared with the 

colour codes illustrated in Figure 1 which all feature 

predominantly vivid colours, typically in ten different 

hues. However, it is not necessary to abandon the 

Colour Zones structure as a point of reference. A 

claimed advantage of the Colour Zones system is that 

it is imprecise and flexible – a colour does not have to 

be the focal colour of a zone in order to represent that 

zone. Each zone contains a range of colours. A yellow– 

orange and a red–orange would be at the edges of the 

Orange zone but still within that zone. In the same way, 

a red–purple and a blue–purple would each be within 

the Purple zone. Furthermore, where a colour is the sole 

representative of a zone it can be moved a short way 

from the focal point of that zone in order to differentiate 

it more clearly from a colour in a neighbouring zone. 

These considerations led to a revision of the palette for 

the colour alphabet. The light and deep blue–greens 

were removed and the vivid orange and vivid purple 

were each sub-divided. There are now seven light 

colours, seven deep colours, ten vivid colours, grey and 

black. 

Assigning colours to letters and naming the colours 

The value of names had been established so the 

colour chosen for each letter should have a name 

beginning with that letter. However, there was another 

consideration that could lead to conflict. During

discussions about how best to assign colours to letters, 

it was suggested that the colours that were easiest to 

identify should be assigned to the letters that occur 

most frequently. In some cases there was no credible 

name for a given colour that might be the best 

choice for a given letter. 

An indication of how frequently letters occur can be 

derived from the numbers on the letter tiles for the 

board game Scrabble [36]. Letters that occur very 

frequently, such as E, R and T, have the number ‘1’ while 

the least common letters, Q and Z, have the number 

‘10’. Another source of information is the website 

AskOxford produced by Oxford University Press [37]. 

An answer to the question about letter frequency is 

provided in the form of a table. This shows how often 

each letter appears in a list of all the words in the 

Concise Oxford Dictionary. 11.16% of the letters are the 

letter E while only 0.196% are the letter Q. In order of 

frequency the letters are: E, A, R, I, O, T, N, S, L, C, U, D, 

P, M, H, G, B, F, Y, W, K, V, X, Z, J, Q. 

An advantage of the Colour Zones system is that the 

colours can be considered to be ‘naturally nameable’ 

in that they are clearly related to shared concepts of 

yellowness, redness, blueness, greenness, whiteness 

and blackness. A colour either corresponds to one of 

the elementary colours, such as Yellow, or it bears equal 

resemblance to such colours. So Orange is equally 

yellowish and reddish and Pink is equally reddish and 

whitish. Also, the Colour Zones already have names 

that can be justified by the research. It was decided 

that these names should be used as far as possible, but 

alternative names were needed where more than one 

name had the same initial letter. It was also decided that 

use should be made of the findings of Rich et al. So B 

should be blue rather than brown or black, G should be 

green rather than grey, R should be red and Y should be 

yellow. A number of alternatives were tested. 

‘A’ is the initial letter for four of the Colour Zones 

names: apricot, azure, aqua and aubergine. ‘A’ is the 

second most commonly occurring letter, so it needed 

to be represented by a colour that would be easily 


identified. Aqua, as a light blue–green, is no longer 

included in the palette. Apricot and aubergine are 

names for colours that were not well identified in the 

study described above. Azure is the name for light blue 

in the Colour Zones system and light blue was identified 

by all participants in the study. However, an alternative 

name for that colour is sky and S is another commonly 

occurring letter. Light blue has been assigned to the 

letter S. Light purple (mauve) was another colour that 

was identified by all participants in the study and there 

is a suitable name for that colour beginning with the 

letter A: amethyst. Black was another colour that was 

correctly identified by all participants in the study, but 

from the findings of Rich et al. black is most commonly 

associated with the letter Z. A quarter of all the 

participants in the original workshop had chosen black 

for the letter Z, while only one had chosen black for the 

letter E. Nevertheless, the letter E is now black. Ease 

of legibility was the overriding criterion and there is a 

suitable name for black which begins with the letter E: 

ebony. 

A consequence of having two colours in the orange 

zone and two in the purple zone was that neither colour 

name should be used. Two new names were needed 

for orange and two for purple. The letter P might have 

been purple but is now pink. With one exception all 

the colour names can be found among the 7500 names 

listed in the Color Names Dictionary published by 

the US Department of Commerce [14]. The exception 

is ‘quagmire’ which is defined in the Concise Oxford 

Dictionary as ‘a soft boggy or marshy area that gives 

way underfoot’ [38]. This seems to be a suitable name 

for a colour that is greenish, yellowish and blackish. The 

revised palette, with colour names and RGB values, 

is shown in Figure 13. No reference was made to the 

names chosen for alphabet colours by Joan Hodsdon at 

the original workshop, but it was interesting to see, after 

the event, that ten of her names feature among those 

selected for the final version of the colour alphabet: 

blue, damson, green, jade, khaki, pink, red, violet, wine 

and yellow.

Key Text 

Chromacons 

The next step was to learn the colour alphabet and 

see if it would be possible to read a text without the 

help of a key. Peter Kovesi, a participant in the original 

workshop, suggested that the coloured rectangles 

representing the letters should have a name. They 

are now called ‘chromacons’. Kovesi wrote a simple 

computer program which enables him to convert 

ordinary text into chromacons. The RGB values for the 

chromacons had to be fine-tuned so that they would be 

sufficiently distinct to be read on computer screens as 

well as in print. 

The words in chromacons are like medal ribbons. The 

experience of reading a text in chromacons was similar 

to that of reading words in Russian after first learning the 

Cyrillic alphabet. At first, each letter had to be identified 

one by one but it began to be possible to recognise 

complete words by their medal-ribbon patterns. In the 

first trial, using the earlier colour alphabet, Kovesi chose 

a sonnet by Shakespeare, rendered it in chromacons 

and sent it as an attachment to an email. It took 13 min 

and 45 s to read and write down the 14 lines of Sonnet 

LIX. The revised colour alphabet was easier to read. 

Kovesi sent six more sonnets and these were read one 

after the other. Times improved from 9 min 53 s for 

the first of these sonnets to 7 min 31 s for the last. No 

doubt reading times would improve even further with 

more practice. In the reading of each sonnet, one or two 

mistakes were made. The confusions were confusions of 

memory rather than perception in all cases but one. The 

blackish red (wine) for W was confused with the brown 

(caramel) for C. Sonnet CXI, that was read in 7 min 31 s, 

is shown in Figure 14. 

There are 467 letters in this sonnet which took 451 

seconds to read and record at just under one second 

per letter. This can be compared with the fastest time 

achieved by a student working with the help of the key. 

The 61 letters were read and recorded in 99 seconds at 

1.62 seconds per letter. 

Discussion 

While it is possible to read a text in chromacons, the 

colour alphabet was never expected to be a serious 

alternative to alphabets made up of shapes. Whether or 

not there is a practical use for a colour alphabet, as an 

alphabet, is an open question. It has been suggested 

that people who have extreme difficulty discriminating 

the shapes of letters might not have the same kind 

of difficulty with colours, but this would need further 

investigation. Meanwhile there remain problems of 

punctuation, accents and how to deal with numerals 

and other symbols. The range of readily distinguishable 

colours seems at the limit with the 26 letters; the system 

would surely break down much beyond that number. 

Differentiation by shape would have to be introduced as 

it is on the London Oyster map. 

The use of colour names might make the colours easier 

to learn for speakers of English but not for speakers of 

other languages. More seriously, and another topic for 

further studies, is the difficulty that would be faced by 

people with defective colour vision who would confuse 

many of the ‘letters’. It seems unlikely that a usable 

colour alphabet could be devised for such people. 

Perhaps the format used for testing the influence of 

simultaneous contrast, illustrated in Figure 12, might be 

developed for investigating vision deficiencies. Of more 

practical value: the colour alphabet could help people 

to establish a basic frame of reference for colours, well 

distributed in colour space. That was the aim when the 

system of Colour Zones was first proposed and the 

alphabet colours are still related to the Colour Zones 

structure. 

For people with normal vision, the colour alphabet 

offers a playful and decorative way to present a text. A 

sonnet rendered in chromacons could even be regarded 

as a work of visual art, just as Shakespeare’s words are 

works of literary art. Such an approach is reminiscent 

of the work of Ellsworth Kelly, Gerhard Richter and 

Damien Hirst as shown in the exhibition ‘Color Chart:

Reinventing Color, 1950 to Today’ presented at the 

Museum of Modern Art in New York in 2008 [39]. 

This project has not provided a definitive answer to 

the transport map problem and it seems unlikely that 

the answer would be a single number. The number 

of different colours that can be discriminated with 

confidence will vary from one situation to another as 

was found by Carter and Carter [16]. The varied success 

of those who participated in the most recent study, 

reported above, suggests that the number of different 

colours that can be identified correctly in a code will 

also depend on the person who is trying to read the 

code. However, it does seem that the limit to the 

number of colours that can be used successfully in a 

colour code is close to 26. Carter and Carter arrived at 

a similar number by different means [13] and Kelly may 

have felt that his proposal of 22 colours of maximum 

contrast was also about the maximum number of colours 

that could be used in a code although he does not 

explain how he arrived at that number [2]. 

A final surprise has come from comparing the colours 

from the colour alphabet with Kelly’s colours of 

maximum contrast. The two sets of colours are shown 

side by side in Figure 15. The extra five alphabet 

colours have been added following Kelly’s principle of 

maximum contrast. Although not perfect, the degree 

of correspondence between the two sets is striking and 

shows how different approaches to a similar problem 

have led to similar conclusions. It could be claimed that 

each result validates the other. 

For purposes of communication, colours are superior 

to shapes in some respects and inferior in others. 

Colour is often the first distinguishing feature used for 

identification and may override differences in shape. 

In a game of cards a player is more likely to mistake a 

heart for a diamond than a diamond for a spade. Colour 

is likely to be the first point of recognition for travellers 

when looking for their bags to appear on the carousel 

in the arrivals area of an airport. A traveller whose bag 


has gone missing is shown a card and asked to point 

to the illustration that corresponds most closely to the 

missing bag [40]. The traveller is also asked to point to 

one from a range of 12 appearance characteristics which 

are shown at the top of the card. These are white/clear, 

black, grey, blue, red, yellow, beige, brown, green, two 

or more solid colours excluding trim, tweed and pattern. 

These are illustrated with examples. There are three 

‘blues’, three ‘reds’, three ‘yellows’ and three ‘greens’. 

There is a turquoise among the blues, a purple among 

the reds, an orange among the yellows and an olive 

among the greens. 

The relative strengths of colours and shapes for 

communicating information are well demonstrated on 

geological maps. The world’s first geological map was 

produce by William Smith and published in 1799. Simon 

Winchester records how Smith considered various 

ways to represent the complexities of the ‘unseen 

underworld’ and concluded that colour was essential 

despite the cost [41]. Colour provides broad information 

about the geological structure and makes it possible to 

read the map. However, the number of different kinds 

of rock that need to be recorded exceeds the number 

that can be represented unambiguously in a colour 

code. Different pinks may be used to identify granite 

of various ages, but the pinks are too similar for certain 

identification without the additional letter–number 

code. For communicating these fine distinctions colours 

alone are no longer adequate. This was Arnheim’s 

essential point when he compared the relative merits 

of colours and shapes and claimed that ‘shape lets 

us distinguish an almost infinite number of different 

individual objects’ [3]. 

Conclusion 

The colour alphabet project was an investigation of 

the limits of colour coding and the relative merits of 

colours and shapes for communicating information. The 

workshop and follow-up studies led to the identification 

of four main factors that contribute to speed and ease of

Key Text 

reading: simplicity, familiarity, nameability and contrast. 

Colours are simpler than shapes, they are familiar and 

they can be named, but the degree of contrast depends 

on how many different colours are used. The colour 

alphabet can be used for text that can be read, but 

only just. Given the different contexts in which colour 

coding is used there may be no definitive answer but, 

for practical purposes, the 26 colours of the alphabet 

can be regarded as a provisional limit – the largest 

number of different colours that can be used before 

colour coding breaks down. These 26 colours can be 

set beside the 25 colours that would be separated by at 

least 51.6 CIE L*u*v* units as shown by Carter and Carter 

[13] and the 22 colours of maximum contrast listed by 

Kelly [2]. Within these limits, colours offer the most 

efficient means of differentiation and identification. 

Beyond these limits colours can still help to differentiate 

and clarify information, as on a geological map, and they 

can be used in partnership with shapes in the way that 

the single lines representing the London Underground 

routes are distinguished from the double lines for the 

mainline routes on the London Oyster map, but shapes 

in some form will be needed. Geological maps, with 

colour coding supplemented by a shape code in the 

form of letters and numbers, illustrate what colours and 

shapes each do best. 

1. P Green-Armytage, Proc. AIC 11th Colour Congress, 

Sydney, Australia (2009). 

2. K Kelly, Color Eng., 3 (6) (1965). 

3. R Arnheim, Art and Visual Perception (Berkeley: 

University of California Press, 1974) 332–333. 

4. L Sivik, personal communication (1983). 

5. Göteborgs linjenät (1995). 

6. Urbantransport-technology (online: http://www. 

urbantransport-technology.com/projects/gothenburg/ 

gothenburg1.html; last accessed, 8 Feb 2010). 

7. Tourist Map of Tokyo (Tokyo: Japan National Tourist 

Organization, 2002). 

8. Paris en Metro (1988). 

9. Paris Metro (online: http://www.paris.org/Metro/gifs/ 

metro.pdf; last accessed, 8 Feb 2010). 

10. London & the South East Rail Services (London: 

Association of Train Operating Companies, 2005). 

11. London Underground Tube Map (London: Transport 

for London, 2005). 

12. Oyster rail services in London (online: http://www.tfl 

.gov.uk/assets/downloads/oyster-rail-services-map. 

pdf; last accessed, 8 Feb 2010). 

13. R Carter and E Carter, Appl. Optics, 21 (1982) 

2936–2939. 

14. K Kelly and D B Judd, Color – Universal Language 

and Dictionary of Names (Washington: US Department 

of Commerce, 1976). 

15. National Bureau of Standards, ISCC-NBS Centroid 

Color Charts (Washington: US Department of 

Commerce, 1976). 

16. R Carter and E Carter, Col. Res. Appl., 13 (1988) 

226–234. 

17. P Campadelli, R Posenato and R Schettini, Col. Res. 

Appl., 24 (1999) 132–138. 

18. C Glasbey, G van der Heijden, V F K Toh and A Gray, 

Col. Res. Appl., 32 (2007) 304-309. 

Reference: GREEN-ARMYTAGE, P., 2010. A colour alphabet and the limits of colour coding. [online] Available at: [Accessed 29/09/10].

A Colour Alphabet and the Limits of Colour Coding Illustrations 

Figure 1 Colour codes for representing the different 

routes on transport maps 

Figure 2 Kelly’s 22 colours of maximum contrast 


Figure 3 Focal colours for levels one and two of the 

Universal Color Language arranged according to 

Munsell hue and lightness value; the shaded areas 

indicate the range of colours that would be named by 

the basic colour terms: white, grey, black, pink, red, 

orange, brown, yellow, green, blue, purple 

Figure 4 Focal colours for levels one and two of the 

Colour Zones system arranged according to hue and 

nuance

Key Text 


Figure 5 Colours to represent the letters of the alphabet 

chosen by participants in a workshop conducted for the 

Colour Society of Australia in 2007 

Figure 6 Nonsense poem with letters represented by 

colours 


shapes (Dingbats)


faces isolated from a group photograph 


Figure 9 Colours from the Colour Zones palette 

assigned to letters for the follow-up studies Figure 10 Sentence with letters represented by colours, 

letters of the Georgian alphabet, faces and 

nameable shapes

Key Text 


Figure 11 Detail of the geological map of Western Australia with part of the key (image courtesy of the Geological 

Survey of Western Australia, Department of Mines and Petroleum © State of Western Australia and reproduced with 

permission)

Figure 12 Sheet used to test people’s ability to 

recognise colours under the influence of simultaneous 

contrast 

Figure 13 Alphabet colours with names and RGB 

values as revised following the study of the influence of 

simultaneous contrast 

Reference: GREEN-ARMYTAGE, P., 2010. A colour alphabet and the limits of colour coding. [online] Available at: [Accessed 29/09/10]. 


Figure 14 Shakespeare’s Sonnet CXI as rendered in 

‘chromacons’ and sent as an email attachment to test 

ease of reading 

Figure 15 Kelly’s 22 colours of maximum contrast set 

beside similar colours from the colour alphabet

Colour 

Theory

The Spectrum 

Isaac Newton’s Prism Experiment, Refracting ‘White’ Light into Colours

Refracting Light with a Diffraction Grating 


Light and Colour 

Universe: Beige, not Turquoise 

The color of the universe is not an intriguing pale 

turquoise, as astronomers recently announced. It’s 

actually beige and a rather ordinary beige at that. 

Two Johns Hopkins University astronomers said in 

January they had averaged all the colors from the 

light of 200,000 galaxies and concluded that if the 

human eye could see this combined hue, it would be a 

sprightly pale green. That, they said, was the color of 

the universe. 

But Karl Glazebrook and Ivan Baldry admitted Thursday 

that their conclusion was wrong. They had been tripped 

up by flawed software that was uncovered by color 

engineers who checked their data. 

“It is embarrassing,” Glazebrook said. “But this is 

science. We’re not like politicians. If we make mistakes, 

we admit them. That’s how science works.” 

The effect of the error was that the computer picked a 

nonstandard white from its electronic palette and mixed 

it with the other colors to come up with the turquoise. 

When the error was corrected and replaced with a 

standard white index, beige was the result, Glazebrook 

said. 

“It looks like beige,” he said. “I don’t know what else to 

call it. I would welcome suggestions.” 

On the website, the authors plead for a name, “as long 

as it is not ‘beige’!” 

In January, Baldry called the turquoise “cosmic 

spectrum green.” But the pair offered no fancy name for 

the new beige hue. 

To find this average color, Glazebrook and Baldry 

gathered light from galaxies out to several billion light 

years. They processed the light to break it into the 

Reference: ANON, 2002b. Universe: Beige, not Turquoise. Wired. [online] Available at: 

[Accessed 14/04/11]. 

various colors similar to how a prism turns sunlight into a 

rainbow. They averaged the color values for all the light 

and converted it to the primary color scale seen by the 

human eye. 

Glazebrook said the underlying data was correct. The 

problem came when the scientific data was converted 

into a hue compatible with the perception of the human 

eye. 

The astronomer said that expressing the color for 

popular viewing was not even part of the original 

scientific experiment. They did it “as a lark.” 

“We were doing this as an amusing footnote to our 

paper,” said Glazebrook. “Then there was a huge media 

thing. We were completely overwhelmed. We didn’t 

expect it to get so big.”

Cosmic Latte 

Cosmic latte is a name assigned to the average color of 

the universe, given by a team of astronomers from Johns 

Hopkins University. 

In 2001, Karl Glazebrook and Ivan Baldry determined 

that the color of the universe was a greenish white, but 

they soon corrected their analysis in a 2002 paper,[1] 

in which they reported that their survey of the color of 

all light in the universe added up to a slightly beigeish 

white. The survey included more than 200,000 galaxies, 

and measured the spectral range of the light from a 

large volume of the universe. The hexadecimal RGB 

value for Cosmic Latte is #FFF8E7. 

The finding of the “color of the universe” was not 

the focus of the study, which was examining spectral 

analysis of different galaxies to study star formation. 

Like Fraunhofer lines, the dark lines displayed in the 

study’s spectral ranges display older and younger stars 

and allow Glazebrook and Baldry to determine the age 

of different galaxies and star systems. What the study 

revealed is that the overwhelming majority of stars 

formed about 5 billion years ago. Because these stars 

would have been “brighter” in the past, the color of the 

universe changes over time shifting from blue to red 

as more blue stars change to yellow and eventually red 

giants. 

Glazebrook’s and Baldry’s work was funded by the 

David & Lucille Packard Foundation. 

As light from distant galaxies reaches the Earth, the 

average “color of the universe” (as seen from Earth) 

marginally increases towards pure white, due to the light 

coming from the stars when they were much younger 

and bluer. 

Naming of the Color 


The color was displayed in a Washington Post article. 

Glazebrook jokingly said that he was looking for 

suggestions for a name for the new color. Several 

people who read the article sent in suggestions. These 

were the results of a vote of the scientists involved 

based on the new color. 

Cosmic Latte Peter Drum 6 

Cappuccino Cosmico Peter Drum 17 

Big Bang Buff/Blush/Beige Many entrants 13 

Cosmic Cream Several entrants 8 

Astronomer Green Unknown 8 

Astronomer Almost Lisa Rose 7 

Skyvory Michael Howard 7 

Univeige Several entrants 6 

Cosmic Khaki Unknown 5 

Primordial Clam Chowder Unknown 4 

Reference: Wikipedia, 2011. Cosmic latte. [online] Available at: [Accessed 13/04/11]. 

Though Drum’s suggestion “Cappuccino Cosmico” 

received the most votes, Glazebrook and Baldry 

preferred Drum’s other suggestion (Cosmic Latte). Drum 

came up with the name while sitting at a Starbucks 

coffee house drinking a latte and reading the Post. 

Drum noticed that the color of the Universe as displayed 

in the Washington Post was the same color as his latte.

Light and Colour 

Redshift 

In physics, redshift happens when light seen 

coming from an object is proportionally increased 

in wavelength, or shifted to the red end of the 

spectrum. More generally, where an observer 

detects electromagnetic radiation outside the visible 

spectrum, “redder” amounts to a technical shorthand 

for “increase in electromagnetic wavelength” — which 

also implies lower frequency and photon energy in 

accord with, respectively, the wave and quantum 

theories of light. 

Redshifts are attributable to the Doppler effect, 

familiar in the changes in the apparent pitches of 

sirens and frequency of the sound waves emitted by 

speeding vehicles; an observed redshift due to the 

Doppler effect occurs whenever a light source moves 

away from an observer. Cosmological redshift is seen 

due to the expansion of the universe, and sufficiently 

distant light sources (generally more than a few 

million light years away) show redshift corresponding 

to the rate of increase of their distance from Earth. 

Finally, gravitational redshifts are a relativistic effect 

observed in electromagnetic radiation moving 

out of gravitational fields. Conversely, a decrease 

in wavelength is called blue shift and is generally 

seen when a light-emitting object moves toward an 

observer or when electromagnetic radiation moves 

into a gravitational field. 

Although observing redshifts and blue shifts have 

several terrestrial applications (e.g., Doppler radar and 

radar guns), redshifts are most famously seen in the 

spectroscopic observations of astronomical objects. 

A special relativistic redshift formula (and its classical 

approximation) can be used to calculate the redshift 

of a nearby object when spacetime is flat. However, 

many cases such as black holes and Big Bang 

Reference: Wikipedia, 2011. Redshift. [online] Available at: [Accessed 08/05/11]. 

cosmology require that redshifts be calculated using 

general relativity.[3] Special relativistic, gravitational, 

and cosmological redshifts can be understood under 

the umbrella of frame transformation laws. There exist 

other physical processes that can lead to a shift in 

the frequency of electromagnetic radiation, including 

scattering and optical effects; however, the resulting 

changes are distinguishable from true redshift and not 

generally referred as such.

The Hubble Telescope Deep Field Image 


Reference: Hubble Site, 1996. Hubble’s Deepest View of the Universe Unveils Bewildering Galaxies across Billions of Years. [press release] January 15, 1996, Available at: 


Colour Theory 

Organising the Spectrum


Reference: STEWART, J., 2010. The Wonderful Colour Wheel. Imprint blog, [blog] 15 July Available at: 


Colour Theory 

Organising the Spectrum 

Reference: STEWART, J., 2010. The Wonderful Colour Wheel. Imprint blog, [blog] 15 July Available at: 


Colour Terminology 

Colour 

Colour is the most exciting design element. Colour has 

three distinct properties: 

Hue - spectral colour name 

Value - lightness or darkness 

Saturation - brightness or dullness 

This part of the course will investigate colour: how you 

see it, how to mix it and a little about how to use it. 

The Eye 

Although you see colour in your brain, it is the eye 

that has the receptors that tell your brain what you are 

looking at. There are two sets of receptors in the retina 

in the back of the eye: rods and cones. 

There are about 125 million rods (named for their shape). 

They are very sensitive to light but are mostly colourblind. 

We use them in dim light and so the saying: “all 

cats are gray in the dark.” 

The colour detectors in the eye are the cones. There 

are about 7 million of these in three forms concentrated 

in the center of vision. Individual cones can only sense 

one of three narrowly defined frequencies of light: 

red, green and blue. The response from these three 

“primary” colours is sorted in our brain to give us the 

perception of colour. One or more of these colour 

receptors malfunctions in a colour-blind person. 

Colour Physics 

Colour is a property of light. Our eyes see only a 

small part of the electromagnetic spectrum. Visible 

light is made up of the wavelengths of light between 

infrared and ultraviolet radiation (between 400 and 700 

nanometers). These frequencies, taken together, make 

up white (sun) light. 

White light can be divided into its component parts 

by passing it through a prism. The light is separated 

by wavelength and a spectrum is formed. Sir Isaac 

Newton was the first to discover this phenomenon in the 

seventeenth century and he named the colours of the 

spectrum. If the ends of the spectrum are bent around 

and joined a colour circle (colour wheel) is formed with 

purple at the meeting place. 

Colour 

Colour has three distinct properties: hue, value and 

saturation. To understand colour you must understand 

how these three properties relate to each other. 

Hue 

The traditional colour name of a specific wavelength of 

light is a hue. Another description is: spectral colour. All 

of the colours of the spectrum are hues. There are only 

limited hue names: red, orange, yellow, green, blue and 

violet. Magenta and cyan are also hues. 

Value 

Value is concerned with the light and dark properties 

of colour. All colours exhibit these properties. The hues 

have a natural value where they look the purest. Some 

colours, like yellow, are naturally light. Some, like violet, 

are darker. 

All hues can be made in all values. Adding white paint 

will make any pigment lighter and are known as tints. 

Adding black paint will make most pigments darker and 

create shades, but will cause yellow paint to shift in hue 

to green. 

Value can exist without hue (see achromatic). Black, 

white and gray are values without colour. Since these

values are used extensively in fashion design, it is 

important to understand their relationship to one 

another. 

Saturation 

Saturation is concerned with the intensity, or the 

brightness and dullness of colour. A saturated colour 

is high in intensity -- it is bright. A colour that is dull 

is unsaturated or low in intensity. Another term for 

saturation is chroma. A colour without any brightness 

(no hue) is achromatic (black, white and/or gray). 

Saturation is the most difficult aspect of colour to 

understand since value and saturation are often 

confused. 

The colour wheel (right) diagrams the relationship 

between hues (around the outside) and saturation 

(center to outside). It is the territory in the center of the 

colour wheel that must be understood in order to be 

able to control the brightness of colors. 

The triangle (left) illustrates the relationship between 

value (vertically) and saturation (horizontally). 

Describing Colours 

All three properties of colour are needed to accurately 

describe a colour. To just say you want “blue” leaves 

many possible choices. Do you mean a greenish blue 

or a more violet blue (hue)? Do you want a light blue, 

or a dark blue (value)? Is it a bright blue or a dull blue 

(intensity)? 


Using a colour name that describes something familiar, 

like robin’s egg blue, is helpful. But would you want the 

bedroom painted too bright if the painter’s robin laid 

different coloured eggs than from yours? Most colour 

names are only vague descriptions and will be different 

for everyone. Try describing your favorite sweater’s 

colour to someone who has not seen it. Could they buy 

yarn to match? Not unless you were very specific about 

hue, value and saturation. 

Albert Munsell developed a system for giving colours 

numerical descriptions. There are five primary and five 

secondary hues in this system. Hue, value and chroma 

are then rated with numbers. Colours can be very 

accurately described using this system. 

Colour is said to be three dimensional because of 

its three aspects: hue value and saturation. A three 

dimensional model using Munsell’s system is called a 

colour tree. The center hub is value (achromatic) with the 

ten hues radiating from it. The colour samples on each 

hue’s vane go from dullest to brightest as they radiate 

from the center out. 

The farther from the center a colour is, the brighter it 

is. Note that each hue is brightest at its natural value: 

yellow is lightest and blue and violet the darkest. 

Colour Theories 

There are two theories that explain how colours work 

and interact. The light, or additive theory deals with 

radiated and filtered light. The pigment, or subtractive 

theory deals with how white light is absorbed and 

reflected off of coloured surfaces. 

Light Theory 

Light theory starts with black -- the absence of light. 

When all of the frequencies of visible light are radiated 

together the result is white (sun) light. The colour 

interaction is illustrated using a colour wheel with red, 

green and blue as primary colours. Primary here means 

starting colors. These are the three colours that the


cones in the eye sense. This is an RGB colour system 

(Red, Green and Blue). 

The primary colours mix to make secondary colours: red 

and green make yellow, red and blue make magenta 

and green and blue make cyan. All three together add 

up to make white light. That is why the theory is called 

additive. 

You can see an example of light theory in action 

almost every day on a computer monitor or a coloured 

television. The same three primary colours are used and 

mixed by the eye to produce the range of colours you 

see on the screen. This theory is also used for dramatic 

lighting effects on stage in a theater. 

Pigment Theory 

Pigments behave almost the opposite of light. With 

pigments a black surface absorbs most of the light, 

making it look black. A white surface reflects most of the 

(white) light making it look white. A coloured pigment, 

green for instance, absorbs most of the frequencies of 

light that are not green, reflecting only the green light 

frequency. Because all colours other than the pigment 

colours are absorbed, this is also called the subtractive 

colour theory. If most of the green light (and only the 

green light) is reflected the green will be bright. If 

only a little is reflected along with some of the other 

colours the green will be dull. A light colour results from 

lots of white light and only a little colour reflected. A 

dark colour is the result of very little light and colour 

reflected. 

The primary colours in the pigment theory have varied 

throughout the centuries but now cyan, magenta and 

yellow are increasingly being used. These are the 

primary colours of ink, along with black, that are used 

in the printing industry. This is a CMYK colour system 

(Cyan, Magenta, Yellow and (K) black). These are the 

secondary colours of the light theory. 

Colour Interaction 

Much has been said about how colours interact. You 

rarely see only one colour. When you see two or more 

colours together they have a profound effect on one 

another. There are a lot of different possibilities but 

these three examples will suffice: 

Colour Temperature 

The colour wheel is useful in that it shows the 

relationship between warm and cool colours. This is 

called colour temperature and relates to the sense of 

temperature each colour imparts. 

The colours on the red side of the wheel are said 

to be warm because they are associated with warm 

phenomena. The green side implies cool phenomena. 

These colour temperature designations are absolute. 

More subtle colour temperature relationships are 

relative. One red can be warmer or cooler than another 

for instance.

Colour temperatures affect us both psychologically and 

perceptually. They help determine how objects appear 

positioned in space. Warm colours are said to advance 

-- they appear closer to the observer. Cool colours are 

said to recede -- they appear farther from the observer. 

Colour Schemes 

There are a number of concepts about colour 

organization but none that will make you a good 

colourist. Colour schemes are descriptions of colour 

relationships, not formulas for using colour well. 

Colour schemes are based on the traditional colour 

wheel. Here are the most common, starting with the 

simplest: 

Achromatic: 

Black, white and the grays in between -- what could be 

simpler. There are no possible colour contrasts. The 

thing to remember is that black and white provide the 

strongest contrast available in fashion. Values must be 

chosen for contrast and visibility with or without colour. 

Chromatic Grays (also known as neutral relief): 

This just means dull colours, sometimes with a hint of 

brightness. Colours near the center of the colour wheel 

are neutral. This scheme is generally harmonious since 

strong hue contrasts are not possible. 

Monochromatic: 

This is like either of the first two but with one dominant 

hue -- a single spoke of the colour wheel. Red, black 

Reference: SAW, J. T. 2001. Colour. [online] Available at: [Accessed 14/04/11]. 


and white is a common example. This is an easy colour 

scheme to set up a basic wardrobe and make up good 

colour combinations. 

Analogous: 

These are related colours from a pie shaped slice of 

the colour wheel. They all have one hue in common 

so things can’t get too wild. Starting with this scheme 

more hue contrasts are possible. This means more 

freedom and expressive potential but it is increasingly 

difficult to make good colour combinations. 

Complementary: 

This scheme uses colours that are opposite on the 

colour wheel (complements). These colours are as far 

apart (hue wise) as colours can be so there is ample 

potential for conflict. Oddly, though, they actually 

can complement each other if used in appropriate 

proportions and with control over saturation. 

Triadic: 

Usually red, yellow and blue -- works for Disney. Actually 

any three more or less equally spaced hues can fit this 

scheme, but why bother. By now you have so many 

colours involved, why not just choose the ones that work 

for what you are trying to communicate in style.


Glossary of Color Terms 

“A”, n - Redness-greenness coordinate in certain 

transformed color spaces (Hunter L,a,b or CIELAB), 

generally used as the difference in “a” between a 

specimen and a standard reference color. If “a” is 

positive, there is more redness than greenness; if “a” 

is negative,there is more greenness than redness. It 

is normally used with b as part of the chromaticity or 

chromaticity color difference. 

Absolute data, n − color measurement data presented 

without comparison of the sample to a standard or 

calculated color difference. 

Absorption, n – (1) penetration of one substance 

into the mass of another. (2) decrease in directional 

transmittance of incident radiation (such as light), 

resulting in a modification or conversion of the 

absorbed energy, into heat, for example. Light incident 

on a specimen may be partially reflected, partially 

transmitted, or partially absorbed. 

absorption tinting strength, n – relative change in the 

absorption properties of a standard white material when 

a specified amount of an absorbing colorant, black or 

chromatic is added to it. 

Accuracy, n – the closeness of agreement between a 

test result and an accepted reference value (often used 

as a color instrument specification). 

Achromatic, adj – (1) for primary light sources, the 

computed chromaticity of the equal-energy spectrum. 

(2) for surface colors, the color of a whitish light, serving 

as the illuminant, to which adaptation has taken place 

in the visual system of the observer. (3) perceived as 

having no hue, that is, as white, gray, or black. 

additive color mixture, n – superposition or other 

nondestructive combination of lights of different 

perceived colors. 

Angle of illumination, n – angle between the specimen 

normal and the illuminator axis. 

angle of view, n – angle between the normal to the 

surface of the specimen and the axis of the receiver. 

aperture, n − the measurement opening in a typical 

reflection color instrument. The size of the aperture 

determines the size and type of sample that can be 

measured. 

Appearance, n – manifestation of the nature of objects 

and materials through visual attributes such as size, 

shape, color, texture, glossiness, transparency, opacity, 

etc. 

Artificial daylight, n – term loosely applied to light 

sources, frequently equipped with filters, which are 

claimed to reproduce the color and spectral distribution 

of daylight. A more specific definition of the light source 

is to be preferred. 

Attributes of color, n – (1) for the object mode of 

appearance, hue, lightness, and saturation. In the 

Munsell system, Munsell Hue, Munsell Value, and 

Munsell Chroma. (2) for the illuminant or aperture mode, 

hue, brightness, and saturation. 

Averaging, vt – a method of color measurement that 

allows you to average several measurements into 

one color measurement. Averaging is recommended 

when measuring standards or samples with surface 

variation. Usually the sample is turned 90° between 

measurements. 

“B”, n – yellowness-blueness coordinate in certain color 

spaces (Hunter L,a,b & CIELAB), generally used as the 

difference in “b” between a specimen and a standard 

reference color, normally used with “a” or “a” as part 

of the chromaticity difference. Generally, if “b” is 

positive, there is more yellowness than blueness; if “b” is 

negative, there is more blueness than yellowness. 

basic color terms, n – a group of 11 color names found 

in anthropological surveys to be in wide use in fully 

developed languages: white, black, red, green, yellow, 

blue, brown, gray, orange, purple, pink. 

Beer’s law, n – the absorbence of a homogeneous 

sample containing an absorbing substance is directly 

proportional to the concentration of the absorbing 

substance, often used in mixture prediction of 

transparent materials. 

Black, n – ideally, the complete absorption of incident 

light; the absence of any reflection. In the practical 

sense, any color that is close to this ideal in a relative 

viewing situation, i.e., a color of very low saturation and 

of low luminance. 

Brightness, n – (1) aspect of visual perception whereby 

an area appears to emit more or less light; (2) of an 

object color, combination of lightness and saturation;

(3) in the textile industry, perceived as saturated, 

vivid, deep, or clean. (color); (4) of paper, reflectance 

of an infinitely thick specimen (reflectivity) measured 

for blue light with a centroid wavelength of 457 nm 

under specified spectral and geometric conditions of 

measurement. (5) dyer’s, the color quality, combining 

lightness and saturation that would be decreased by 

adding black, gray, or a complementary color to a 

chromatic dye. 

Bronzy color (or bronzing), n – a metallic coloration 

observed when viewing the light reflected at angles 

near the angle of specular reflection, the color usually 

being quite different from that observed for other 

directions (i.e., paint samples). 

C or Delta c, n – abbreviations for chromaticity or 

chromaticity difference, respectively. 

Calibrate, vt – to find and eliminate systematic errors 

of an instrument scale or method of instrument by 

use of material standards and techniques traceable to 

an authorized national or international measurement 

system. 

Characterize, vt – to specify the parameters or 

performance of an instrument, method of measurement, 

or material absorption and scatter. 

Chroma, n – (1) attribute of color used to indicate the 

degree of departure of the color from a gray of the 

same lightness. (2) C*, (in the CIE 1976 L*, a*, b* or L*, u*, 

v* system) the quantity C*ab = (a*2 + b*2)1/2 or C*uv 

= (u*2 + v*2)1/2. (3) attribute of a visual perception, 

produced by an object color that permits a judgment to 

be made of the amount of pure chromatic color present, 

irrespective of the amount of achromatic color. 

chromatic, adj – perceived as having a hue; not white, 

gray, or black. (opposite of achromatic) 

Chromaticity, n – dimensions of a color stimulus 

expressed in terms of hue and saturation, or rednessgreenness 

and yellowness-blueness, excluding the 

luminous intensity; generally expressed as a point in a 

plane of constant luminance. Syn: chromaticness. 

chromaticity coordinates, CIE, n -- the ratios of each of 

the three tristimulus values X, Y and Z in relation to the 

sum of the three; designated as x, y and z, respectively. 

They are sometimes referred to as the trichromatic 


coefficients. When written without subscripts, they 

are assumed to have been calculated for Illuminant C 

and the 2° (1931) Standard Observer unless specified 

otherwise. If they have been obtained for other 

illuminants or observers, a subscript describing the 

observer or illuminant should be used. For example, 

x10 and y10 are chromaticity coordinates for the 10° 

observer and Illuminant C. 

Chromaticity diagram, CIE, n – a two-dimensional 

graph of the chromaticity coordinates, x as the abscissa 

and y as the ordinate, which shows the spectrum locus 

(chromaticity coordinates of monochromatic light, 380- 

770 nm). It has many useful properties for comparing 

colors of both luminous (light emitting) and nonluminous 

(reflective) materials. 

CIE, n – the abbreviation for the French title of the 

International Commission on Illumination, Commission 

Internationale de l’Eclairage. 

CIE 1931 standard observer, n – ideal colorimetric 

observer with color matching functions x-(y), y-(y), 

z-(y) corresponding to a field of view subtending a 2º 

angle on the retina; commonly called the “2º standard 

observer.” 

CIE 1964 supplementary standard observer, n – ideal 

colorimetric observer with color matching functions 

x-10(y), y-10(y), z-10(y) corresponding to a field of view 

subtending a 10º angle on the retina; commonly called 

the “10º standard observer.” 

CIELAB color difference, n – color difference calculated 

by using the CIE 1976 L* a* b* opponent-color scales 

(also referred to as CIELAB), based on applying a cuberoot 

transformation to CIE 1931 tristimulus values X, Y, Z 

or CIE 1964 tristimulus values X10, Y10, Z10. 

Clarity, n – the characteristic of a transparent body 

whereby distinct high-contrast images or high-contrast 

objects (separated by some distance from the body) are 

observable through the body. 

Cmc (l:c) color difference, n – color difference 

calculated by use of the formula developed by the 

Colour Measurement Committee of the Society of Dyers 

and Colourists of Great Britain. Based on the lightness, 

hue, chroma version of CIELAB, it incorporates chroma 

and hue-angle correction terms for improved visual


spacing and variable weighting factors for lightness (l) 

and chroma (c) relative to hue for improved correlation 

depending on type of judgment (acceptability, 

perceptibility) and application (textiles, others). CMC 

reports the equivalent of Δ E as a weighted function of 

the ratio of L:Ch as Performance Factor=PF. 

Color, n – (1) of an object, aspect of object appearance 

distinct from form, shape, size, position, or gloss that 

depends upon the spectral composition of the incident 

light, the spectral reflectance of transmittance of the 

object, and the spectral response of the observer, 

as well as the illuminating and viewing geometry. 

(2) perceived, attribute of visual perception that 

can be described by color names such as white, 

gray, black, yellow, brown, vivid red, deep reddish 

purple, or by combinations of such names. (3) 

psychophysical, characteristics of a color stimulus (that 

is, light producing a sensation of color) denoted by a 

colorimetric specification with three values, such as 

tristimulus values. 

Colorant, n – dye, pigment, or other agent used to 

impart a color to a material. 

color atlas, n – a collection of color samples arranged 

according to a color order system (such as Munsell, 

NCS, DIN). 

Color constancy, n – the general tendency of the colors 

of an object to remain constant when the color of the 

illumination is changed. 

Color difference, n – (1) perceived, the magnitude 

and character of the difference between two colors 

described by such terms as redder, bluer, lighter, darker, 

grayer, or cleaner. (2) computed, the magnitude and 

direction of the difference between two psychophysical 

color stimuli and their components computed from 

tristimulus values, or chromaticity coordinates and 

luminance factor, by means of a specified set of colordifference 

equations. 

Color-difference units, n – units of size of the color 

differences calculated according to various equations. 

Such color differences cannot be accurately converted 

between different equations by the use of average 

factors. 

Colorimeter tristimulus, n – instrument that 

measures psychophysical color, in terms of tristimulus 

values, by the use of filters to convert the relative 

spectral power distribution of the illuminator to 

that of a standard illuminant, and to convert the 

relative spectral responsively of the receiver to the 

responsivities prescribed for a standard observer. See 

spectrocolorimeter. 

Colorimetry, n – the science of color measurement. 

colorist, n -- a person skilled in the art of color matching 

(colorant formulation) and knowledgeable concerning 

the behavior of colorants in a particular material; a tinter 

(q.v.) (in the American usage) or a shader. 

Color match, n – (1) condition existing when colors 

match within a specified or agreed tolerance. 

Sometimes called commercial color match. (2) condition 

existing when colors are indistinguishable; a normal 

observer is usually implied. Sometimes called an exact 

color match. 

Color matching, n – procedure for providing, by 

selection, formulation, adjustment, or other means, 

a trial color that is indistinguishable from, or within 

specified tolerances of, a specified standard color under 

specified conditions. 

Color-matching functions, n – the amounts, in any 

trichromatic system, of the three-reference color stimuli 

needed to match by an additive mixture monochromatic 

components of an equal energy spectrum. 

Color measurement, n – physical measurement of light 

radiated, transmitted, or reflected by a specimen under 

specified conditions and mathematically transformed 

into standardized colorimetric terms that can be 

correlated with visual evaluations of color relative to 

one another. Although the term “color measurement” is 

normally used, color itself cannot be measured. 

color order systems, n – a rational method or plan of 

ordering and specifying all object colors, or all within a 

limited domain, by means of a set of material standards 

selected and displayed so as to represent adequately 

the whole set of object colors under consideration. 

Color perception, n – subjective impression of color, 

as modified by the conditions of observation and by 

mental interpretation of the stimulus object.

Color space, n – a geometric space, usually of three 

dimensions, in which colors are arranged systematically. 

color specification, n – notation or set of three 

color-scale values used to designate a color in a 

specified color system. Practical color specifications 

may include color tolerances as well as target color 

designation, measuring instrument, instrument settings, 

measurement procedures and sample preparation 

procedures. 

Color stimulus, n – a radiant flux capable of producing a 

color perception. 

Color temperature, n – of a light source, the 

temperature, usually expressed in kelvins, of a full 

radiator (perfect, theoretical black substance that when 

heated would not affect the “color” of the light emitted) 

which emits light of the same chromaticity as the source. 

For example average daylight color temperature is often 

expressed as D65, for 6,500 degrees Kevin. 

color tolerance, n – the permissible color difference 

between sample and specified color (Standard). 

color tolerance set, n – a group of colored standards, 

usually seven painted chips, arranged on a single card, 

one exhibiting a desired color, and two each exhibiting 

the limits of the permissible range of color variation in 

each of the color attributes. Normally the set is arranged 

as the acceptable limits in lightness to darkness, redness 

to greenness, yellowness to blueness. 

Contrast, n – objective, the degree of dissimilarity of 

a measured quantity such as luminance of two areas, 

expressed as a number computed by a specified 

formula. 

Contrast ratio, n – see, contrast or opacity. In color 

measurement, a sample is measured over a white 

background, then over a black background and the 

ratio expressed either as a perctentage (where 100% = 

complete opacity) or as a ratio to 1. 

Crazing, n – a network of apparent fine cracks on or 

beneath the surface of materials, such as in transparent 

plastics, glazed ceramics, glass, or clear coatings. 

daylight illuminants, CIE, n -- series of illuminant spectral 

power distribution curves based on measurements of 

natural daylight and recommended by the CIE in 1965. 


Values are defined for the wavelength region 300 to 

830 nm. They are described in terms of the correlated 

color temperature. The most important is D6500 

(often referred to as average daylight) because of the 

closeness of its correlated color temperature to that of 

Illuminant C = 6774 K. D7500, bluer than D6500 and 

D5000, yellower than D6500, are also used. 

Densitometer, n – instrument designed for measuring 

optical density of a photographic negative or positive or 

a printed image; not suitable for colorimetry. 

densitometry, n – technique for measurement of optical 

density by use of a densitometer. 

Detector, n – device to convert radiant energy (light) 

into a neural signal (such as the eye) or an electrical 

signal (such as a phototube, photomultiplier tube, 

photocell, photodiode, or the like). 

Diffuse reflection, n – reflection in which flux is 

scattered in many directions by diffusion at or below the 

surface. 

Directionality, n – (1) perceived, the degree to which 

the appearance of a surface changes as the surface 

is rotated in its own plane, under fixed conditions of 

illumination and viewing. In color the addition of metal 

flake and or pearlescent pigments will greatly increase 

the visual color effects. Surface striation and texture 

may also greatly change color appearance. (2) measured 

–- (scattering indicatrix, azimuthally nonisotropic) – 

difference in pattern of near-specular and semidiffusely 

scattered light, dependent upon the azimuthal angles of 

the incident and viewing beams. 

Dominant wavelength, n – the wavelength of a 

spectrally pure light that, when added to a reference 

achromatic (white) light, will produce a combination that 

matches the color of a specimen light. 

Delta Δ ,n − indicates deviation or difference. 

Delta E, Delta e, Δ E, Δ e n -- the total color difference 

computed with a color difference equation. It is 

generally calculated as the square root of the sum of the 

squares of the chromaticity difference, Δ a + Δ b, and 

the lightness difference, Δ L; in CMC identified as the 

“commercial factor” (CF). 

Eggshell, adj – semi-matte, having a texture resembling 

that of the outer surface of the shell of a chicken egg.


Usually used in describing a type of paint finish. 

Fading, n – a change in color, usually to a lighter and 

less-saturated color, normally over time caused by the 

effects of light and other environmental effects on the 

colorants in a material. 

Flat, adj – (1) of a coating material, a material that is 

capable of imparting a finish free of gloss. (2) of a 

surface finish, free of gloss. 

Flop, n – a difference in color and appearance of a 

material viewed over two widely different aspecular 

angles. 

Flop angle, n – the aspecular angle when a material is 

viewed from a direction far from the specular, typically 

70º or more, normally associated with a change in color 

and appearance at two viewing angles. 

Fluorescence, n – a process by which radiant flux 

of certain wavelengths is absorbed and reradiated 

nonthermally at other, usually longer, wavelengths. (this 

phenomenon creates colors that show an abnormal 

color response and are used for packaging and other 

dramatic effects). 

Fluorescent illuminant, n – illuminant representing the 

spectral distribution of the radiation from a specified 

type of fluorescent lamp (as expressed as a set of data 

in color computer programs). 

FMC-2 color difference, n – color difference calculated 

by use of the Friele-MacAdam-Chickering, Version 

2, equations based on the MacAdam chromaticitydifference-perceptibility 

ellipses and the Munsell value 

function. 

Foot candle, n – unit of illuminance equal to one lumen 

per square foot. 

Gardner color scale, n – a color scale for clear, lightyellow 

fluids, defined by the chromaticities of glass 

standards numbered from 1 for the lightest to 18 for the 

darkest. 

Gloss, n – angular selectivity of reflectance, involving 

surface-reflected light, responsible for the degree to 

which reflected highlights or images of objects may be 

seen as superimposed on a surface. 

Gloss meter, n – instrument that measures surfacereflected 

light at defined angles (i.e., 60º, 85º). 

Gray scale, n – an achromatic scale ranging from black 

through a series of successively lighter grays to white. 

Such a series may be made up of steps that appear to 

be equally distant from one another (such as the Munsell 

Value Scale) or may be arranged according to some 

other criteria such as a geometric progression based 

on lightness. Such scales may be used to describe 

the relative amount of difference between two similar 

colors. 

Goniospectrophotometer, n – spectrophotometer 

having the capability of measuring with a 

variety of illuminating and viewing angles using 

bidirectional geometry; also known as multi-angle 

spectrophotometer. Usually used to measure colored 

samples with great directionality. 

Haze, n – in reflection, (1) scattering of light at the glossy 

surface of a specimen responsible for the apparent 

reduction in contrast of objects viewed by reflection at 

the surface. (2) percent of reflected light scattered by 

a specimen having a glossy surface so that its direction 

deviates more than a specified angle from the direction 

of specular reflection. 

Hiding power, n – (1) the ability of a coating material 

to hide the surface coated by producing a specified 

opacity at a given film thickness. The greater the 

amount of scattering pigments, the greater the hide. (2) 

the area over which a specified volume of paint can be 

spread to produce a specified contrast between areas 

where the substrate is black and where it is white. 

Hue, n – the attribute of color by means of which a color 

is perceived to be red, yellow, green, blue, purple, etc. 

Pure white, black, and grays possess no hue. 

Hunter color difference, n – color difference calculated 

by the use of the Hunter equations, based on the 

opponent-color coordinates, L, a, b, applied to CIE 1931 

tristimulus values for CIE standard illuminant C, and by 

extension to the CIE 1964 standard observer and other 

CIE standard illuminants. 

Illuminant, n – mathematical description of the relative 

spectral power distribution of a real or imaginary light 

source, that is, the relative energy emitted by a source 

at each wavelength in its emission spectrum (the data 

entered into a color computer, used to predict the effect 

of different light sources on the perceived color).

Illuminant A (CIE), n – incandescent illumination, yellow 

orange in color, with a correlated color temperature of 

2856K. It is defined in the wavelength range of 380-770 

nm. 

Illuminants D (CIE), n – daylight illuminants, defined 

from 300-830 nm, the UV portion 300-380 nm being 

necessary to describe correctly colors which contain 

fluorescent dyes or pigments. They are designated 

as D with a subscript to describe the correlated 

color temperature: D65 having a correlated color 

temperature of 6504K, close to that of Illuminant C, is 

the most commonly used. They are based on actual 

measurements of the spectral distribution of daylight. 

Illuminator, n – the portion of a radiometric or 

photometric instrument that provides the illuminating 

beam on the specimen, including the source, 

occasionally the monochromator or spectral filters, 

a diffuser such as an integrating sphere, if used, and 

associated optics. 

Index of refraction, n – the numerical expression of the 

ratio of the velocity of light in a vacuum to the velocity 

of light in a substance (gas, liquid, solid), at a specified 

wavelength. 

Integrating sphere, n – an optical device used either to 

collect light reflected or transmitted from a specimen 

into a hemisphere or to provide isotropic irradiation of 

a specimen from a complete hemisphere, consisting of 

an approximately spherical cavity with apertures (ports) 

for admitting and detecting light, and usually having 

additional apertures over which sample and reference 

specimens are placed and for including or excluding 

the specularly reflected components (surface reflected 

light). 

Interference filter, n – filter constructed of extremely 

thin alternate layers of high and low refractive-index 

material and capable of transmitting narrow spectral 

bands formed by constructive interference within the 

desired waveband and destructive interference at 

other wavelengths (used in filter colorimeters and some 

abridged spectrophotometers). 

Just-perceptible difference, n – color difference that 

is just large enough to be perceived by an observer in 

almost every trial. 


Kubelka-Munk theory, n – phenomenological 

turbid-medium theory relating the reflectance and 

transmittance of scattering and absorbing materials to 

optical constants (Kubelka-Munk absorption coefficient 

(K), Kubelka-Munk scattering coefficient (S)) and the 

concentrations of their colorants. (The basis of computer 

color-matching calculations 

LED, light emitting diode, n − solid state light emitters 

that are extremely stable and durable, the latest 

technology in color instrument light sources. 

Light, n – (1) electromagnetic radiation of which a 

human observer is aware through the visual sensations 

that arise from the stimulation of the retina of the eye. 

This portion of the spectrum includes wavelengths from 

about 380 nm to 780 nm. Thus, it is incorrect to speak 

of ultraviolet or infrared “light” because the human 

observer cannot see radiant energy in the ultraviolet 

and infrared regions. (2) light, adj – referring to the color 

of a non-self-luminous body, having a high luminous 

reflectance factor, as “light green” or “light gray.” 

lightfastness, n – the ability of a material to withstand 

color change on exposure to light. 

Lightness, n – (1) the attribute of color perception by 

which a non-self-luminous body is judged to reflect 

more or less light. (2) the attribute by which a perceived 

color is judged to be equivalent to one of a series of 

grays ranging from black to white. 

Light source, n – an object that emits light or radiant 

energy to which the human eye is sensitive. The 

emission of a light source can be described by the 

relative amount of energy emitted at each wavelength 

in the visible spectrum, thus defining the source as an 

illuminant, or the emission may be described in terms of 

its correlated color temperature. 

Lovibond tintometer, n – instrument for evaluating the 

colors of materials by visual comparison with the colors 

of glasses of the Lovibond color system. 

luminescence, n – emission of light ascribable to 

nonthermal excitation. 

Luster, n – the appearance characteristic of a surface 

that reflects more in some directions than it does in 

other directions, but not of such high gloss as to form 

clear mirror images.


Masstone, n – in paint technology, a pigment-vehicle 

mixture containing a single colorant only. Discussion 

– At times colorants are developed or recycled that 

contain more than one pigment, but that are tested and 

used as if they contained only a single pigment. This 

definition is meant to include such colorants. 

Match, vt – to provide, by selection, formulation, 

adjustment, or other means, a trial color that is 

indistinguishable from, or within specified tolerances of, 

a specified standard color under specified conditions. 

matte, n – lacking luster or gloss. Synonymous with 

“flat” in paint terminology. 

Metameric, adj – (1) pertaining to spectrally different 

objects or color stimuli that have the same tristimulus 

values. (2) pertaining to objects, having different 

spectrophotometric curves that match when illuminated 

by at least one specific illuminant (viewing condition) 

and observed by a specific observer. 

Metamerism, n – property of two specimens that 

match under a specified illuminator (illuminant) and to a 

specified observer and whose spectral reflectances or 

transmittances differ in the visible wavelengths and may 

appear to be a miss match under a second specified 

illuminant to the same specified observer. 

Munsell color system, n – a system of specifying 

colors of surfaces illuminated by daylight and viewed 

by an observer adapted to daylight, in terms of three 

attributes: hue, value, and chroma, using scales that are 

perceptually approximately uniform. 

Munsell notation, n – (1) the Munsell hue, value, 

and chroma assigned to the color of a specimen by 

visually comparing the specimen to the chips in the 

Munsell Book of Color. (2) a notation in the Munsell 

color system, derived from luminous reflectance Y and 

chromaticity coordinates x and y in the 1931 CIE system 

for standard illuminant C, by the use of scales defined 

by the Optical Society of America Subcommittee on the 

Spacing of the Munsell Colors. 

Natural Color System, n – color order system based 

on resemblance’s of colors to up to four of six 

“elementary” colors red, yellow, green, blue, black, 

and white, in which the attributes of the colors are hue, 

chromaticness, and blackness. 

Neutral, adj – achromatic or without hue. 

Observer, n – the human viewer who receives a 

stimulus and experiences a sensation from it. In vision, 

the stimulus is a visual one and the sensation is an 

appearance. 

Observer metamerism, n – the property of specimens 

having different spectral characteristics and having the 

same color when viewed by one observer, but different 

colors when viewed by a different observer under the 

same conditions. 

Opacity, n – (1) optical, the ability of a specimen to 

prevent the transmission of light; the reciprocal of the 

transmittance factor. (2) paper backing, the ability of a 

sheet of paper to hide a surface behind and in contact 

with it, expressed as the ration of the reflectance factor 

Rb when the sheet is backed by a black surface to the 

reflectance factor Roo when it is backed by a pile of 

sheets of the same kind, and of such number that further 

addition of sheets does not affect the measured opacity. 

(3) white backing, the ability of a thin film or sheet 

of material, such as paint or paper, to hide a surface 

behind and in contact with it, expressed as the ratio of 

the reflectance factor Rb when the material is backed 

by a black surface to the reflectance factor Rw when it is 

backed by a white surface (usually having a reflectance 

factor of 0.89.) 

Opaque, adj – transmitting no optical radiation, you can 

not see through the material. 

Opponent-color scales, n – scales that denote one color 

by positive scale values, the neutral axis by zero value, 

and an approximately complementary color by negative 

scale values. Common examples include scales that are 

positive in the red direction and negative in the green 

direction (CIE a*, Hunter a) and scales that are positive 

in the yellow direction and negative in the blue direction 

(CIE b*, Hunter b). 

Orange peel, n – the appearance of irregularity of a 

surface resembling the skin of an orange, usually on 

painted surfaces. 

Pearlescent, adj – a colorant exhibiting various colors 

depending on the angles of illumination and viewing, as 

observed in mother-of-pearl.

Petroleum color scale, n – a color scale for petroleum 

products, defined by 16 glass standards of specified 

luminous transmittance and chromaticity, graduated in 

steps of 0.5 from 0.5 for the lightest color to 8.0 for the 

darkest. 

Photochromism, n – a reversible change in color of a 

specimen due to exposure to light. 

Photometer, n – an instrument for measuring light. 

Photopic, adj – pertaining to human vision at sufficiently 

high levels of illumination that only the retinal cones are 

stimulated. 

Physical standard, n – stable specimen having a value of 

a physical quantity assigned by accurate measurements 

under specified conditions, usually in a standards 

laboratory. 

Port, n – an opening or aperture in an integrating 

sphere. 

Precision, n – the closeness of agreement between test 

results obtained under prescribed conditions. 

Primary colorants, n – a small number (pallet) of 

colorants (dyes or pigments) that may be mixed to 

produce a large gamut of colors. 

Primary standard, n – a physical standard calibrated by 

an absolute method. 

Product standard, n – material having a color 

designated as standard for a specified product. 

Radiant energy, n – the form of energy consisting of 

the electromagnetic spectrum that travels at 115.890 

kilometers/s (186.500 miles/s) through a vacuum, 

reducing this speed in denser media (air, water, glass, 

etc.). The nature of radiant energy is described by its 

wavelength or frequency, although it also behaves as 

distinct quanta (“corpuscular theory”). The various 

types of energy may be transformed into other forms 

of energy (electrical, chemical, mechanical, atomic, 

thermal, radiant) but the energy itself cannot be 

destroyed. 

Receiver, n – the portion of a photometric instrument 

that receives the viewing beam from the specimen, 

including a collector such as an integrating sphere, if 

used, often the monochromator or special filters, the 

detector, and associated optics and electronics. 


Reference standard, n – a physical standard used to 

calibrate a group of laboratory standards. 

Reflectance, n – the ratio of the intensity of reflected 

radiant flux to that of the incident flux. In popular 

usage, it is considered as the ratio of the intensity of 

reflected radiant energy to that reflected from a defined 

reference standard. 

Reflectance, specular, n – reflectance of a beam of 

radiant energy at an angle equal but opposite to 

the incident angle: the mirror-like reflectance. The 

magnitude of the specular reflectance on glossy 

materials depends on the angle and on the difference in 

refractive indices between two media at a surface and 

may be calculated from the Fresnel Law. 

Reflection, n – of radiant energy (in the case of color, 

light), the process by which radiant energy is returned 

from a material or object. 

Refraction, n – change in the direction of light 

determined by change in the velocity of the light in 

passing from one medium to another. 

Repeatability, n – (1) the closeness of agreement 

between the results of successive measurements of 

the same test specimen, or of test specimens taken at 

random from a homogeneous supply, carried out on a 

single laboratory, by the same method of measurement, 

operator, and measuring instrument, with repetition 

over a specified period of time. This is the most 

important aspect of sample presentation technique 

and one of the most important specifications for a 

color instrument. (2) the ability to create and replicate a 

formula in a consistent manner as defined by a defined 

tolerance. 

Reproducibility, n – the closeness of agreement 

between the results of successive measurements of 

the same test specimen, or of test specimens taken at 

random from a homogeneous supply, but changing 

conditions such as operator, measuring instrument, 

laboratory, or time. The changes in conditions must be 

specified. 

Retroreflector, n – a reflecting surface or device from 

which, when directionally irradiated, the reflected 

rays are preferentially returned in directions close to 

the opposite of the direction of the incident rays, this


property being maintained over wide variations of the 

direction of the incident rays. Most commonly used in 

highway signage materials. 

Saturation, n – the attribute of color perception that 

expresses the degree of departure from the gray of 

the same lightness. All grays have zero saturation. 

Commonly used as a synonym for chroma especially in 

graphic arts. 

Scattering, n – diffusion or redirection of radiant energy 

encountering particles of different refractive index; 

scattering occurs at any such interface, at the surface, or 

inside a medium containing particles. 

scattering tinting strength, n – relative change in the 

scattering properties of a standard black material (with 

no scattering colorant present) when a specified amount 

of a white or chromatic scattering colorant is added to 

it. 

Scotopic, adj – pertaining to vision at sufficiently low 

levels of illumination that only the retinal rods are 

stimulated. 

Shade, n – (1) a color produced by a dye or pigment 

mixture including black dye or pigment. (2) an 

expression of color difference from a reference dyeing 

such that another dye must be added to produce a 

match. (3) a color slightly different from a reference 

color. 

Shade, vt – to adjust the color of a test specimen to be 

a closer color match to the standard. 

shade sorting, n – process of grouping together, 

often by instrumental measurement, similarly colored 

materials so that the materials within each group may be 

used together in a finished product without perceived 

color variation. 

Sheen, n – the specular gloss at a large angle of 

incidence for an otherwise matte specimen (used in 

textiles). 

Source, n – an object that produces light or other 

radiant flux. 

Spectral, adj – for radiometric quantities, pertaining to 

monochromatic radiation at a specified wavelength or, 

by extension, to radiation within a narrow wavelength 

band about a specified wavelength. 

Spectral characteristic, n – the reflectance, reflectance 

factor, transmittance, or transmittance factor as 

a function of wavelength, used to characterize a 

specimen. 

Spectral power distribution curve, n – intensity of 

radiant energy as a function of wavelength, generally 

gives in relative power terms. 

Spectrocolorimeter, n – spectrophotometer, one 

component of which is a dispersive element (such 

as prism, grating, or interference filter or wedge) 

that is normally capable of producing as output only 

colorimetric data (such as tristimulus values and derived 

color coordinates) but not the underlying spectral data 

from which colorimetric data are derived. 

Spectrogoniophotometer, n – goniophotometer having 

the capability of measuring as a function of wavelength. 

See the preferred term, goniospectrophotometer. 

Spectrometer, n – an instrument for measuring a 

specified property as a function of a spectral variable. In 

optical radiation measurements, the spectral variable is 

wavelength or wavenumber and the measured property 

is (or is related to) absorbed, emitted, reflected, or 

transmitted radiant power. 

Spectrophotometer, n – photometric device for the 

measurement of spectral transmittance, spectral 

reflectance, or relative spectral emittance. 

spectrophotometry, n – quantitative measurement of 

reflection or transmission properties as a function of 

wavelength. 

Spectroradiometer, n – a spectrometer for measuring 

emitted optical radiant power, normally of a light source. 

Spectrum Spatial arrangement of components of radiant 

energy in order of their wavelengths, wave number or 

frequency. 

Spectrophotometric curve, n – a curve measured 

on a spectrophotometer: hence a graph of relative 

reflectance or transmittance (or absorption) as the 

ordinate, plotted versus wavelength or frequency as the 

abscissa. In color, usually covering the practical visual 

range from 400-700 nm. 

Specular, adj – pertaining to flux (light) reflected from 

the surface of an object, without diffusion, at the 

specular angle.

Specular gloss, n – relative luminous fractional 

reflectance from a surface in the mirror or specular 

direction. It is sometimes measured at 60° relative to a 

perfect mirror. 

Specular reflection, n – reflection without diffusion, in 

accordance with the laws of optical reflection, as in a 

mirror. 

Specular reflectance excluded (SCE), n – measurement 

of reflectance made in such a way that the specular 

reflectance is excluded from the measurement: diffuse 

reflectance. The exclusion may be accomplished by 

using 0º (perpendicular) incidence on the samples, 

thereby reflecting the specular component of the 

reflectance back into the instrument, by use of black 

absorbers or light traps at the specular angle when the 

incident angle is not perpendicular, or in directional 

measurement by measuring at an angle different from 

the specular angle. Used where surface finish is an 

important component of a color measurement. 

Specular reflectance included (SCI), n – measurement 

of the total reflectance from a surface, including the 

diffuse and specular reflectance (usually in a sphere 

instrument). Used when the surface finish is not critical 

to color measurement results. 

Standardize, vt – to adjust instrument output to 

correspond to a previously established calibration using 

one or more homogeneous specimens or reference 

materials. (calibrate, verify) This is the normal condition 

that is often referred to as “field calibration” of a color 

instrument. 

Standard observer, n – an ideal observer having 

visual response described by the CIE color-matching 

functions. 

Strength, n – (1) the color quality that increases 

with an increase in the amount of colorant present, 

other conditions remaining constant. (2) in reflection 

colorants, a series of calculations based on the relative 

absorption of a given colorant. 

Subtractive color mixture, n – mixture of absorbing 

media or superposition of filters so that the spectral 

composition of light passing through the combination is 

determined by simultaneous or successive absorption. 


Surround, n – portion of the visual field immediately 

surrounding the central field or pattern of interest. 

Texture, n – the visible surface structure depending on 

the size and organization of small constituent parts of 

a material; typically, the surface structure of a woven 

fabric or surface finish of a painted part. 

Thermochromism, n – a change in color with 

temperature change. adj - thermochromatic 

Tint, n – the color produced by the mixture of white 

pigment with absorbing (generally chromatic) colorants. 

The color of the resulting mixture is lighter and less 

saturated than the color without the addition of the 

white. 

Tint, vt – to adjust the color of a test specimen to be a 

closer color match to the standard. 

Tolerance, n − the range of color difference that is 

acceptable to say that the color is a commercial match. 

Most usually an agreement is made between buyer and 

seller concerning color acceptability. 

Transfer standard, n – a physical standard used to 

transfer a calibration from one instrument to another, 

usually from a reference instrument in a standards 

laboratory to an instrument in the field. 

Translucency, n – the property of a specimen by which it 

transmits light diffusely without permitting a clear view 

of objects beyond the specimen and not in contact with 

it. 

Translucent, adj – transmitting light diffusely, but not 

permitting a clear view of objects beyond the specimen 

and not in contact with it. 

Transmission, n – of radiant energy, the process 

whereby radiant energy passes through a material or 

object. 

Transparency, n – the degree of regular transmission, 

thus the property of a material by which objects may be 

seen clearly through a sheet of it. 

Transparent, adj – adjective to describe a material that 

transmits light without diffusion or scattering. 

Tristimulus, n – of, or consisting of, three stimuli: 

generally used to describe components of additive 

mixture required to evoke a particular color sensation. 

tristimulus values,match n – the amounts of the three 

specified human response stimuli required to match a 

color.


Tristimulus values, CIE, n – amounts (in percent) of the 

three components necessary in a three-color additive 

mixture required for matching a color; in the CIE System, 

they are designated as X, Y and Z. The illuminant and 

standard observer color matching functions used must 

be designated; if they are not, the assumption is made 

that the values are for the 1931 observer (2º field) 

and Illuminant C. The values obtained depend on the 

method of integration used and on the relationship of 

the nature of the sample and on the instrument design 

used to measure the reflectance or transmittance. 

Tristimulus values are not, therefore, absolute values 

characteristic of a sample, but relative values dependent 

on the method used to obtain them. Approximations 

of CIE tristimulus values may be obtained from 

measurements made on a tristimulus colorimeter, giving 

measurements generally normalized to 100, which must 

then be normalized to equivalent CIE values. The filter 

measurements should be properly designated as R, G, 

and B instead of X, Y, and Z. 

Tungsten, light source, n − (1) constant burning 

tungsten, traditional light source in color instruments. 

(2) flashing tungsten, latest low cost light source, 

typically used in portable color instruments. 

Turbidity, n – reduction of transparency of a specimen 

due to the presence of particulate matter. 

ultraviolet, adj – referring to radiant flux having 

wavelengths shorter than the visible wavelengths, about 

10 nm to 380 nm. 

Uniform-chromaticity-scale diagram, n – chromaticity 

diagram on which all pairs of just-perceptibly different 

colors of equal luminance are represented by pairs of 

points separated by nearly equal distances. 

Uniform color space, n – schematic arrangement of 

colors in space in which spatial intervals between 

points correspond to visual differences between colors 

represented by those points (goal of all color space/ 

order systems that has yet to be achieved). 

verification standard, n – calibrated physical 

standard used to verify the accuracy of calibration 

of measurement scales, operating characteristics, or 

system responses of color-measuring instruments. 

Verify, vt – to assess the overall reliability and accuracy 

of an instrument or method of measurement by use of 

material standards for which the measurable quantities 

have accepted values. 

Viewing conditions, n – the conditions under which 

a visual observation is made, including the angular 

subtense of the specimen at the eye, the geometric 

relationship of light source, specimen, and eye, the 

photometric and spectral character of the light source, 

the photometric and spectral character of the field 

of view surrounding the specimen, and the state of 

adaptation of the eye. 

Visible, adj – pertaining to that portion of the 

electromagnetic spectrum to which the eye is sensitive, 

approximately 390 to 710 nm in wavelength. 

Visibility, n – the properties and behavior of light waves 

and objects interacting in the environment to produce 

light signals capable of evoking visual sensation. 

Visual colorimeter, n – an instrument using the eye as 

detector that measures color stimuli produced by mixing 

one or more of at least three primary colors. 

Visual perception, n – the visual experience resulting 

from stimulation of the retina and the resulting activity 

of associated neural systems. 

Vavelength, (y), n – of an electromagnetic wave, the 

distance in the direction of propagation between 

nearest points at which the electric vector has the 

same phase; the distance as expressed in nanometers 

(nm, billionth of a meter) of the wavelength period or 

frequency (peak to peak or trough to trough) of the 

wavelength in question. The visible spectrum spans 

400-700m. 

Whiteness, n – attribute of color perception by which an 

object color is judged to approach the preferred white. 

whiteness index, n – a number, computed by a given 

procedure from colorimetric data that indicates the 

degree of departure of an object color from a preferred 

white. 

Working standard, n – (1) an instrument standard or 

laboratory standard in routine use. (2) a standard that 

is almost identical to the laboratory standard and the 

color difference is exaclty known from the laboratory 

standard.

Xenon, light source, n − high energy, pulsed light used 

in color instruments. 

Yellowness, n – attribute of color perception by which 

an object color is judged to depart from colorless or a 

preferred white toward yellow. 

Yellowness index, n – a number, computed by a given 

procedure from colorimetric or spectrophotometric data 

that indicates the degree of departure of an object color 

from colorless, or from a preferred white, toward yellow. 

X One of the three CIE tristimulus values; the red 

primary. 

Y One of the three CIE tristimulus values; equal to 

the luminous reflectance or transmittance ; the green 

primary 

Z One of the three CIE tristimulus values; the blue 

primary. 

Reference: Colortec, nd. Glossary of color terms. [online] Available at: [Accessed 14/04/11]. 


Colour 

Perception

Colour Perception 

Online Farnsworth Munsell 100 Hue Test


Reference: X-rite, 2010. Online Colour Challenge. [online] Available at: [Accessed 28/11/10].


First Picture of Living Human Retina Reveals Surprise by Sara Goudarzi 

The first images of living human retinas showing the 

wide diversity of number of cones sensitive to different 

colors. The image on the left shows the retina of a 

person who has very few red-sensing cones, and the 

one on the right is of someone with far more red cones. 

The pictures are blurry because that’s the best the new 

imaging process could manage. 

The first images of living human retinas showing the 

wide diversity of number of cones sensitive to different 

colors. The image on the left shows the retina of a 

person who has very few red-sensing cones, and the 

one on the right is of someone with far more red cones. 

The pictures are blurry because that’s the best the new 

imaging process could manage. Photo 

credit: University of Rochester 

The first images ever made of retinas in living people 

reveal surprising variation from one person to the next. 

Yet somehow our perceptions don’t vary as might be 

expected. 

Imaging thousands of cells responsible for detecting 

color in the deepest layer of the eye, scientists found 

that our eyes are wired differently. Yet we all -- with the 

exception of the color blind -- identify colors similarly. 

The results suggest that the brain plays an even more 

significant role than thought in deciding what we see. 

Inside the Eye 

The eye, responsible for receiving visual images, 

is wrapped in three layers of tissue [graphic]. The 

innermost layer, the retina, is responsible for sensing 

color and sending information to the brain. 

The retina contains light receptors known as cones 

and rods. These receptors receive light, convert 

it to chemical energy, and activate the nerves 

that send messages to the brain. The rods are in 

charge of perceiving size, brightness and shape of 

images, whereas color vision and fine details are the 

responsibility of the cones. 

On average, there are 7 million cones in the human 

retina, 64 percent of which are red, 32 percent green, 

and 2 percent blue, with each being sensitive to a 

slightly different region of the color spectrum. At least 

that’s what scientists have been saying for years. 

But the first complete imaging of the human retina, 

mapping the arrangement of the three types of cone 

photoreceptors, revealed something surprising about 

these numbers. 

Big Variation 

The study found that people recognized colors in the 

same way. Yet the pictures of their retinas showed there 

is enormous variability, sometimes up to 40 times, in the 

relative number of green and red cones in the retina. 

“[This] suggests that there is a compensatory 

mechanism in our brain that negates individual 

differences in the relative numbers of red and green 

cones that we observed,” Joseph Carroll, a researcher at 

Center for Visual Science at University of Rochester and 

a collaborator of the study, told LiveScience. 

The researchers used adaptive optics imaging, which 

uses a camera containing a corrective device that 

cancels the effects of the eye’s imperfect optics on 

image quality, producing a high-resolution retinal 

picture. 

Borrowing from Astronomy 

“Adaptive optics is a technique borrowed from 

astronomy where it is used to obtain sharp images 

of stars from telescopes on the ground,” said David 

Williams, Director of Center for Visual Science at the 

University of Rochester. “All such telescopes suffer 

from blur due to the effects of turbulence in the Earth’s 

atmosphere. In our case, optical defects in the cornea 

and lens of the eye blur images of the retina.”

The measured defects were corrected using 

deformable mirrors, which bend and morph according 

each person’s eye, before taking high magnification 

pictures of the eye. This allowed Williams and 

colleagues to see and map single cells such as the 

cones. 

The researchers hope to use the same techniques to 

better understand various forms of color blindness and 

different kinds of retinal disease. 

The findings were detailed in a recent issue of the 

Journal of Neuroscience. 


Reference: GOUDARZI, S., 2005. First Picture of Living Human Retina Reveals Surprise. Live Science blog, [blog] 28 November Available at: [Accessed 11/10/10].


Lotto Lab Public Colour Experiments 

Conceptual background 

Colour is the simplest perception the brain has 

(even brainless jellyfish see lightness), and yet colour 

underpins so much of human activity – and indeed 

evolution itself. We live in a world shaped by colour. It 

influences what you eat, what you buy and how you live. 

‘What is colour?’ and ‘How do we see colour?’ are 

the crucial questions at the heart of these public 

experiments, and indeed are part of a major programme 

of research at lottolab. 

The Research Programme 

Working in collaboration with the BBC’s Horizon 

programme, during the filming of ‘Do you see what I 

see?’(lottolab’s most recent contribution to the BBC2 

science series), we launched a series of high-to-low level 

experiments to answer these questions about colour. 

The answers have been very exciting and challenge 

typical views of colour perception. What is more, this 

programme of research has not only directly engaged 

the public – illustrating our idea that the best form of 

public engagement of science is science itself – but 

has also confirmed the scientific merit of running real 

science in a public space. 

To determine the inter-personal differences between 

people’s perception of colour, hundreds of subjects 

are required. Only in this way is it possible to find 

relationships between what we see and who we are (our 

personal state of being: sex, age, race, culture, status, 

etc). However, to do such experiments in a conventional 

lab would require months and months – experiments 

that we were able to complete in a matter of days in our 

lab at the Science Museum. 

Reference: Lotto Lab, 2011. [online] Available at: [Accessed 09/08/11]. 

The Experiments 

Seven different experiments on colour were conducted, 

addressing questions that are basic at one level and 

more cognitive/high-level at the other. These questions 

include: 

• How long does a minute last, and how does colour 

affect our perception of time? 

• Does one’s sense of status alter one’s lower-level 

perceptions? 

• Are we all affected by illusions to the same extent? 

• Are emotions coloured? 

• Are there common perceptual spaces in the brain 

that cross modality – between shape and colour, 

sound and colour, words and colour? 

• What are the effects of status on the abstractness of 

images we make?

Lotto Lab Public Perception Project 

The Context 

Thousands of scientific papers are published every year 

about the nature of perception. The vast majority of 

those papers describe research conducted by people 

living at US universities, and in Western universities 

more generally. Which means that nearly all the subjects 

taking part in these experiments – as many as 90 per 

cent – are white, middle-class undergraduate students. 

From these observations, scientists (and the public) 

generalize their findings on these specific kinds of 

people to the whole of the human species. 

While the science in many of these papers is of high 

value, the generalization of the findings is not. People 

are not all the same… or are they? Does the person 

next to you see the world the same way that you do? 

What about your child, what about children raised in 

the far North where the visual ecology – and culture – is 

mathematically very different from that experienced by 

children raised near the equator? 

The Project 

If we are truly to understand what it means to be human, 

and understand the three-way relationship between 

ecology, brain structure and behaviour – the ‘Holy 

Grail’ of neuroscience – then doing research in the 

conventional way will not give us the answers. Instead, 

if we are to understand the inter-personal differences 

between people, we must perform highly controlled 

experiments on thousands of subjects around the world. 

Much like the ‘Human Genome Project’, then, the 

tremendous ambition of the (human) Public Perception 

Project is to discover the differences and similarities 

across all humans. Because perception underpins 

all human behaviour, in a very real sense, then, the 

ambition is to define human perception. 

Given the potential importance of the vast amount of 

data we will obtain about human perception on a largescale, 

we will make our ‘normative’ databases available 

Reference: Lotto Lab, 2011. [online] Available at: [Accessed 09/08/11]. 


to all researchers, since such a database has the 

tremendous potential for not only understanding how 

we see the world, but also as a diagnostic tool. 

A Thousand…Million Subjects? 

The ambitions of the Public Perception Project are one 

of the key reasons why lottolab is located at the Science 

Museum, since the Science Museum attracts literally 

millions of people from all over the world into its space. 

Which means that all these visitors become our potential 

subjects. 

But then this location and endeavour raises another 

question. Which is this… how do you run valuable, 

controlled experiments on thousands of people in a 

public space? How long do the experiments have to 

be? How ‘noisy’ is the data? Does it require different 

experimental methods and statistics? What do your 

visitors/subjects/participants want or need in return 

to feel engaged? At this level, is there a meaningful 

boundary between science research and art, since 

science approached in this way becomes both research 

and performance, data-gathering and a sensory 

experience. 

Public Engagement 

In short, our public perception project is itself a research 

programme into public engagement, which explores 

our hypothesis that the best form of engagement is 

science itself. It’s also a critical aspect of our ‘Science 

in the Museum’ initiative, as it demonstrates explicitly 

the scientific merit of using public spaces as a place 

for doing real science (if only one knew what was 

required to do it well), which could tour nationally and 

internationally.


Magenta Ain’t A Colour by Liz Elliott 

A beam of white light is made up of all the colours in the 

spectrum. The range extends from red through to violet, 

with orange, yellow, green and blue in between. But 

there is one colour that is notable by its absence. 

Pink (or magenta, to use its official name) simply isn’t 

there. But if pink isn’t in the light spectrum, how come 

we can see it? 

Here’s an experiment you can try: stare at the pink circle 

below for about one minute, then look over at the blank 

white space next to the image. What do you see? You 

should see an afterimage. What colour is it? 

You should have seen a green afterimage, but why is this 

significant? 

The afterimage always shows the colour that 

is complementary to the colour of the image. 

Complementary colours are those that are exact 

opposites in the way the eye perceives them. 

It is a common misconception that red is complementary 

to green. However, if you try the same experiment 

as above with a red image, you will see a turquoise 

afterimage, since red is actually complementary to 

turquoise. Similarly, orange is complementary to blue, 

and yellow to violet. 

All the colours in the light spectrum have complements 

that exist within the spectrum – except green. There 

seems to be some kind of imbalance. What is going on? 

Is green somehow being discriminated against? 

The light spectrum consists of a range of wavelengths 

of electromagnetic radiation. Red light has the longest 

wavelength; violet the shortest. The colours in between 

have wavelengths between those of red and violet light. 

When our eyes see colours, they are actually detecting 

the different wavelengths of the light hitting the retina. 

Colours are distinguished by their wavelengths, and the 

brain processes this information and produces a visual 

display that we experience as colour. 

This means that colours only really exist within the brain 

– light is indeed travelling from objects to our eyes, 

and each object may well be transmitting/reflecting a 

different set of wavelengths of light; but what essentially 

defines a ‘colour’ as opposed to a ‘wavelength’ is 

created within the brain. 

If the eye receives light of more than one wavelength, 

the colour generated in the brain is formed from the 

sum of the input responses on the retina. For example, if 

red light and green light enter the eye at the same time, 

the resulting colour produced in the brain is yellow, the 

colour halfway between red and green in the spectrum.

So what does the brain do when our eyes detect 

wavelengths from both ends of the light spectrum at 

once (i.e. red and violet light)? Generally speaking, it has 

two options for interpreting the input data: 

a) Sum the input responses to produce a colour halfway 

between red and violet in the spectrum (which would 

in this case produce green – not a very representative 

colour of a red and violet mix) 

b) Invent a new colour halfway between red and violet 

Magenta is the evidence that the brain takes option b – 

it has apparently constructed a colour to bridge the gap 

between red and violet, because such a colour does not 

exist in the light spectrum. Magenta has no wavelength 

attributed to it, unlike all the other spectrum colours. 

The light spectrum has a colour missing because it does 

not feel the need to ‘close the loop’ in the way that our 

brains do. We need colour to make sense of the world, 

but equally we need to make sense of colour; even if 

that means taking opposite ends of the spectrum and 

bringing them together. 

Reference: ELLIOT, L. nd. Magenta ain’t a colour. [online] Available at: [Accessed 02/07/11]. 


Well, now we’ve got that sorted out, explain this: stare 

at the dot in the middle of the image below - you should 

see all the colours melt away.


Colour Blindness 

Color blindness or color vision deficiency is the inability 

or decreased ability to see color, or perceive color 

differences, under lighting conditions when color vision 

is not normally impaired. “Color blind” is a term of art; 

there is no actual blindness but there is a fault in the 

development of either or both sets of retinal cones that 

perceive color in light and transmit that information to 

the optic nerve. The gene that causes color blindness 

is carried on the X chromosome, making the handicap 

far more common among men (who have just one X 

chromosome) than among women (who have two, so 

must inherit the gene from both parents). 

The symptoms of color blindness also can be produced 

by physical or chemical damage to the eye, optic nerve, 

or the brain generally. These are not true color blindness, 

however, but they represent conditions of limited actual 

blindness. Similarly, a person with achromatopsia, 

although unable to see colors, is not “color blind” per se 

but they suffer from a completely different disorder, of 

which atypical color deficiency is only one manifestation. 

The English chemist John Dalton published the first 

scientific paper on this subject in 1798, “Extraordinary 

facts relating to the vision of colours”, after the realization 

of his own color blindness. Because of Dalton’s work, the 

condition was often called daltonism, although this term 

is now used for a single type of color blindness, called 

deuteranopia. 

Color blindness is usually classed as a mild disability but 

there are situations where color blind individuals can 

have an advantage over those with normal color vision. 

Some studies conclude that color blind individuals are 

better at penetrating certain color camouflages; this may 

be an evolutionary explanation for the surprisingly high 

frequency of congenital red–green color blindness. 

The average human retina contains two kinds of light 

cells: the rod cells (active in low light) and the cone cells 

(active in normal daylight). Normally, there are three kinds 

of cones, each containing a different pigment, which are 

activated when the pigments absorb light. The spectral 

sensitivities of the cones differ; one is maximally sensitive 

to short wavelengths, one to medium wavelengths, 

and the third to long wavelengths, with their peak 

sensitivities in the blue, yellowish-green, and yellow 

regions of the spectrum, respectively. The absorption 

spectra of all three systems cover the visible spectrum. 

These receptors are often called S cones, M cones, and L 

cones, for short, medium, and long wavelength; but they 

are also often referred to as blue cones, green cones, 

and red cones, respectively. 

Although these receptors are often referred to as “blue, 

green, and red” receptors, this terminology is not very 

accurate, especially as the “red” receptor actually has 

its peak sensitivity in the yellow region. The sensitivity 

of normal color vision actually depends on the overlap 

between the absorption spectra of the three systems: 

different colors are recognized when the different types 

of cone are stimulated to different degrees. Red light, 

for example, stimulates the long wavelength cones 

much more than either of the others, and reducing the 

wavelength causes the other two cone systems to be 

increasingly stimulated, causing a gradual change in hue. 

Many of the genes involved in color vision are on the X 

chromosome, making color blindness more common 

in males than in females because males have only one 

X chromosome, while females have two. Because this 

is an X-linked trait about 1% of women have a 4th color 

cone and can be considered tetrachromats although 

it is not clear that this provides an advantage in color 

discrimination. 

Color vision deficiencies can be classified as acquired 

or inherited. There are three types of inherited or 

congenital color vision deficiencies: monochromacy, 

dichromacy, and anomalous trichromacy.

• Monochromacy, also known as “total color 

blindness,” is the lack of ability to distinguish colors; 

caused by cone defect or absence. Monochromacy 

occurs when two or all three of the cone pigments 

are missing and color and lightness vision is reduced 

to one dimension. 

• Rod monochromacy (achromatopsia) is an 

exceedingly rare, nonprogressive inability to 

distinguish any colors as a result of absent or 

nonfunctioning retinal cones. It is associated with 

light sensitivity (photophobia), involuntary eye 

oscillations (nystagmus), and poor vision. 

• Cone monochromacy is a rare total color blindness 

that is accompanied by relatively normal vision, 

electoretinogram, and electrooculogram. 

• Dichromacy is a moderately severe color vision 

defect in which one of the three basic color 

mechanisms is absent or not functioning. It is 

hereditary and, in the case of Protanopia or 

Deuteranopia, sex-linked, affecting predominantly 

males. Dichromacy occurs when one of the cone 

pigments is missing and color is reduced to two 

dimensions. 

• Protanopia is a severe type of color vision deficiency 

caused by the complete absence of red retinal 

photoreceptors. It is a form of dichromatism in which 

red appears dark. It is hereditary, sex-linked, and 

present in 1% of males. 

• Deuteranopia is a color vision deficiency in which 

the green retinal photoreceptors are absent, 

moderately affecting red–green hue discrimination. 

It is a form of dichromatism in which there are only 

two cone pigments present. It is likewise hereditary 

and sex-linked. 

• Tritanopia is a very rare color vision disturbance in 

which there are only two cone pigments present and 

a total absence of blue retinal receptors. 

• Anomalous trichromacy is a common type of 

inherited color vision deficiency, occurring when one 

of the three cone pigments is altered in its spectral 


sensitivity. This results in an impairment, rather than 

loss, of trichromacy (normal three-dimensional color 

vision). 

• Protanomaly is a mild color vision defect in which an 

altered spectral sensitivity of red retinal receptors 

(closer to green receptor response) results in poor 

red–green hue discrimination. It is hereditary, sexlinked, 

and present in 1% of males. 

• Deuteranomaly, caused by a similar shift in the green 

retinal receptors, is by far the most common type of 

color vision deficiency, mildly affecting red–green 

hue discrimination in 5% of males. It is hereditary and 

sex-linked. 

• Tritanomaly is a rare, hereditary color vision 

deficiency affecting blue–yellow hue discrimination. 

Unlike most other forms, it is not sex-linked. 

Reference: Wikipedia, 2011. Color blindness. [online] Available at: [Accessed 11/09/11].


Half of the Women See More Colors Than the Rest of the People Do by Stefan Anitei 

Normally, people have three types of cone cells for 

daylight, for detecting different colors. But some women 

can see extra colors as they have four types of cone cell 

receptors. They are called tetrachromats. Compared to 

them, we all are color blind. 

The first tetrachromat woman was discovered by 

researchers at Cambridge University in 1993. This is 

perhaps the most remarkable human mutation ever 

detected. The fact that all tetrachromats are female 

intrigued scientists. Now two scientists, working 

separately, want to investigate systematically for 

tetrachromats to clarify more about their existence and 

how they detect colors. 

All mammals descended from nocturnal tree dwellers, 

which were colorblind, but the line of primates had more 

advantages in developing color vision for finding fruit 

food. Human color vision is based on three forms of 

iodopsin (color pigments), each sensitive to a different 

light wavelength and is found in a different cone type. 

When a different cone type is stimulated, the brain reads 

it as a particular color. 

The three iodopsins respond to red, green and blue; all 

the other colors are their combinations. Like all pigments, 

iodopsins are proteins encoded by DNA genes. The 

genes encoding the “red” and “green” iodopsins are 

located on the X sex chromosome, while the “blue” 

iodopsin is on a non-sexual chromosome. 

That’s why color-blindness mostly affects men: 8% of the 

Caucasian males; while under 0.5 % of Americana women 

present it. Women have X chromosomes: one from the 

mother and one the father, while men have just one X 

chromosome from the mother and an Y sex chromosome 

from the father (this one does not contain any iodopsin 

gene). 

X chromosomes can be a “green” iodopsine or a slightly 

shifted “green” iodopsine, and a “red” iodopsine and a 

shifted “red” iodopsine. That’s why a woman can carry 

5 types of iodopsins: these four plus “blue”, while a man 

just three (a green type, a red one plus blue). 

A recent paper by Kimberly Jameson, Susan Highnote 

and Linda Wasserman of the University of California, San 

Diego, showed that up to 50 % of women carry 4 types 

of iodopsins and can employ their extra pigments in 

“contextually rich viewing circumstances”. 

For example, when looking at a rainbow, these females 

can segment it into about 10 different colors, while 

trichromat (with three iodopsins) people can see just 

seven: red, orange, yellow, green, blue, indigo and 

violet. For tetrachromat women, green was found to be 

assigned in emerald, jade, verdant, olive, lime, bottle and 

34 other shades. 

Still, the birds’ abilities are even superior. Pigeons have 

five color receptors (and five types of cell receptors) and 

can process visual information up to 10 times faster than 

human beings. While we see a smooth TV image in real 

movement and color, they will see dull flickering lights. 

Tetrachromats species are encountered among birds, 

insects, jumping spiders, reptiles, and amphibians, but no 

mammal is known to posses this. Some of them detect 

UV light. 

Color-blindness means the lack of the ability to 

distinguish a certain color. The term is somewhat of 

a misnomer, as color perception is diminished, not 

eliminated. Real color-blindness, wherein a person can 

distinguish no color at all, requires an impairment of all 

three types of color receptors, and is found in just 0.003% 

of the population. 

Dr. Gabriele Jordan of Cambridge University tested the 

color perception of 14 women who each had at least one 

son with the right kind of color-blindness. In a test, the 

subjects had to manipulate and blend two wavelengths 

of colored light to produce any hue they liked, and after 

that, they had to test their own results a second time.

With normal tricolor vision, several different combinations 

would match any given hue, with a tetrachromat the 

visible match would be much decreased. 2 of the 14 

subjects showed exactly the results expected from a 

tetrachromat. One of the two reported having a different 

sense of color from the people around her, with a better 

color matching and color memory. 

Some suggest that the tetrachromats are born with 

four types of cone cells. One research pointed out that 

2-3% of the world’s women may have the kind of fourth 

cone that lies between the standard red and green 

cones. Mutation in iodopsine genes is common in most 

human populations, and tetrachromacy could be linked 

to major red-green pigment mutations, linked to “color 

blindness” (protanomaly or deuteranomaly). 


Reference: ANITEI, S., 2007. Half of the Women See More Colors Than the Rest of the People Do. [online] Available at: 


Colour Perception Theory 

The Data Problem for Color Objectivism by Donald D. Hoffman 

Are colors objective or subjective? Are they properties, 

processes, or events of the physical world or, instead, 

of the perceiving subject? This question has been 

debated at least since the time of Galileo and remains 

unsettled to this day. Evidence from computational and 

psychophysical studies of vision has not decided the 

issue, with both objectivists and subjectivists claiming 

that the evidence to date is in their favor. 

In his article, ‘‘The Location Problem for Color 

Subjectivism,’’ Peter Ross proposes that color 

subjectivists make two mistakes, one logical and 

one empirical (Ross, 2001). The logical mistake is an 

unwitting commitment to a philosophical assumption 

he calls the ‘‘corresponding category constraint.’’ The 

empirical mistake is the failure of any subjectivist theory 

to properly account for recent data on sensed locations. 

Ross concludes that color subjectivism is untenable and 

proposes instead that disjunctive physicalism is the most 

viable remaining candidate. 

Here I argue that the data on sensed locations are 

different than Ross claims and that once the data 

are properly understood they pose no obstacle to 

adverbial-subjectivist theories. Then I argue that 

disjunctive physicalist accounts of color need the 

corresponding category constraint no less than 

subjectivist accounts or else they are devoid of empirical 

support. Finally I raise an empirical challenge for color 

subjectivists and a separate empirical challenge for 

disjunctive physicalists. 

Ross raises the problem of sensed locations as 

an empirical obstacle to acceptance of adverbialsubjectivist 

theories. According to such theories, 

sensing is not a relation between a perceiver and 

sense data or other objects, but rather a nonrelational 

way that a perceiver is. When a perceiver sees a red 

square he sees redly and squarely; when he sees a 

green circle he sees greenly and roundly. Seeing redly, 

greenly, squarely, and roundly describe kinds of mental 

processes or events of the perceiver. 

The problem with this theory, according to Ross, is 

that colors have sensed locations, and the theory 

cannot account for the empirical data on the binding 

of colors with locations in the visual field. We can, for 

instance, see a red circle inside a green square and 

distinguish this from a green square inside a red circle. 

The adverbialist must provide a nonrelational account 

of sensed locations to handle such cases, and the most 

straightforward way is to describe the visual field as an 

array of repeatable sensory events to which sensory 

adverbs, such as redly, can apply. Then he can describe 

one sensory event as redly and roundly and insidely 

and another as greenly and squarely and outsidely 

and thus resolve the problem of sensed locations. But 

this account of sensed locations as repeatable sensory 

events leads to the prediction that the same sensed 

location can qualify different parts of the visual field, 

e.g., the prediction that the same sensed location can 

qualify both a red circle and a green square. In this case, 

says Ross, we should be able to find disorders where the 

same sensed location does qualify different parts of the 

visual field. And, he claims, no such disorders have been 

identified. Therefore the adverbialist account of sensed 

location founders on the empirical evidence. 

Here I think Ross has the empirical evidence wrong. 

One need not be disordered to have the same sensed 

location qualify different parts of the visual field. Indeed, 

multiple qualification is easily demonstrated with normal 

perceivers. I can, for instance, create a computer display 

in which red dots are randomly placed within a disk 

and green dots randomly placed within a square. I then 

rigidly translate the two sets of dots past each other, 

say the green dots moving to the left and the red dots 

to the right. Normal observers see two transparent 

shapes, a square and a disk, moving past each other 

at the same sensed location. Thus the same sensed 

location qualifies a square moving to the right and a 

disk moving to the left, no disorders required. And it is 

straightforward to construct many other examples using 

perceived transparency. The phenomenon of perceived 

transparency is exactly what one would predict from

the adverbialist theory: One sensed location qualifying 

multiple parts of the visual field. 

One might object that in this example it is not the 

same sensed location that qualifies both a square and 

a disk, since the square and disk are usually seen at 

slightly different depths. In reply, I could note that it 

is debatable whether they are always seen at slightly 

different depths. But let us grant the point. We can 

modify the example by rotating both disk and square 

about the vertical axis, the disk by 145° and the square 

by 245° and then have them slide past each other 

in depth. As they slide they meet in a vertical line of 

intersection, and along this line the disk and square 

have the same sensed location, not just in 2D but also in 

3D. So this one line qualifies both a square at 145° and a 

disk at 245°. And once again we have the same sensed 

location qualifying different parts of the visual field. 

I conclude that, whether or not adverbialist theories 

are correct, they cannot be dismissed on the empirical 

grounds claimed by Ross. Instead the empirical data 

on multiple qualification are just as predicted by 

adverbialists. 

Now I turn to argue that disjunctive physicalist accounts 

of color need the corresponding category constraint 

no less than subjectivist accounts, or else they are 

devoid of empirical support. According to Ross, the 

corresponding category constraint is the following: 

‘‘colors are identified with a range of properties which 

corresponds with and explains our ordinary color 

categories.’’ Ross observes that many subjectivists 

tacitly assume the corresponding category constraint 

in their arguments for subjectivism. This constraint, he 

claims, should be rejected, along with the arguments for 

subjectivism that it supports. Instead he endorses the 

view that colors are disjunctive physical properties. They 

must be disjunctive because the existence of metamers 

shows that widely different physical situations are 

experienced as the same sensed color. 


Here is my argument: 

Premise 1: (Denial of corresponding category 

constraint). Colors are not identified with a range of 

properties which corresponds with and explains our 

ordinary color categories. 

Premise 2: (Ross’s definition of ordinary color 

categories). Ordinary color categories are the categories 

by which we classify colors as qualitatively identical or 

different and qualitatively similar or dissimilar. 

Premise 3: (Disjunctive physicalism). Colors are 

identified with disjunctive physical properties. 

Conclusion 1: The disjunctive physical properties that 

are identified with colors do not explain the categories 

by which we classify colors as qualitatively identical or 

different and qualitatively similar or dissimilar. 

Premise 4: If theory A makes no claim to explain data 

set B, then data set B does not constrain theory A. 

Conclusion 2: The disjunctive physical properties 

identified with colors are not constrained by judgements 

of color similarity or identity. 

The question naturally arises: What empirical data do 

constrain the disjunctive physical properties? One 

possible answer is: none. But no one is interested 

in a theory with no empirical constraints. Another 

possible answer is: serial search experiments, attention 

experiments, ... , but not experiments using judgements 

of similarity or identity. But this is ad hoc. Why should 

some psychophysical evidence be admitted and some 

not? What are the principled grounds for deciding 

which psychophysical evidence to admit, while rejecting 

judgements of similarity and identity. There are none. 

A third possible answer is: No psychophysical data 

constrain the disjunctive physical properties, but data 

from other sciences, such as physics and chemistry, do 

constrain them. This answer is desperate and ad hoc. 

What principle guides the choice of constraining data? 

None.

Colour Perception Theory 

I do not conclude from this that disjunctive physicalism 

is untenable. I simply conclude that if one wants to buy 

disjunctive physicalism, then one had better also buy 

the corresponding category constraint or else be left 

with no plausible empirical support. The subjectivists 

and disjunctive physicalists have this in common: 

They both equally need the corresponding category 

constraint to support their theories. 

Now I raise a challenge for color subjectivists. Many 

subjectivists conclude that physical objects are 

colorless. They support this claim in part by noting the 

existence of metamers, which cannot be explained 

by physical categories but probably can be explained 

by neural processes. Now ‘‘metamers’’ occur not just 

for colors, but also for shapes, motions, textures, 

positions, and a host of other visual properties. There 

are countless different stimuli that can lead one to 

see the same 3D shape (Hoffman, 1998), just as there 

are countless different metamers. Similarly there are 

countless different stimuli that lead one to see the 

same motion, or texture, or position. If one concludes 

from the existence of color metamers that physical 

objects are colorless, then consistency demands that 

one also conclude that physical objects are also without 

position, shape, motion, or texture. I do not view this as 

an argument against color subjectivism. It is simply an 

argument that if one opts for color subjectivism, then 

one should be prepared to go all the way with all other 

visual properties as well. 

Finally I raise a challenge for disjunctive physicalists, 

whom I now take to embrace the corresponding 

category constraint. The empirical data on color that 

must be accounted for by a disjunction of physical 

properties is enormous and diverse. It includes the fact 

that, simply rearranging the relative positions of colored 

squares can make them appear entirely different colors 

(Hoffman, 1998, p. 112); that observers can be induced 

to see a white patch of paper as any other color by 

using the technique of neon color spreading (Hoffman, 

1998, p. 135); that, colors can be seen in regions of 

space devoid of any tangible objects (Hoffman, 1998, p. 

138); and that observers can be induced to see a white 

patch of computer screen as any other color by using 

the technique of color from motion (http:/ /aris.ss.uci. 

edu). Disjunctive physicalism cannot be accepted simply 

because the alternative, color subjectivism, is claimed 

to be implausible. To be taken seriously, disjunctive 

physicalism must propose specific disjunctions of 

physical properties that do justice to the plethora of 

color data just mentioned and more besides. To date I 

have seen no proposed disjunction that is even remotely 

plausible. 

References 

Ross, P. W. (2001). The location problem for color 

subjectivism. Consciousness and Cognition, 10, 42– 

58. 

Hoffman, D. D. (1998). Visual intelligence: How we create 

what we see. New York: Norton. http:/ / 

aris.ss.uci.edu/cogsci/personnel/hoffman/Colordiskexp. 

html: ‘‘Color From Motion Illusion.’’ 

Reference: HOFFMAN, D., 2001. The data problem for colour objectivism. [online] Available at: [Accessed 

24/08/10].

Colour 

Systems

Language and Number Systems 

Colour Language Versus Numbers – Munsell Colour System 

In colorimetry, the Munsell color system is a color space 

that specifies colors based on three color dimensions: 

hue, value (lightness), and chroma (color purity). It 

was created by Professor Albert H. Munsell in the first 

decade of the 20th century and adopted by the USDA 

as the official color system for soil research in the 1930s. 

Several earlier color order systems had placed colors 

into a three dimensional color solid of one form or 

another, but Munsell was the first to separate hue, value, 

and chroma into perceptually uniform and independent 

dimensions, and was the first to systematically illustrate 

the colors in three dimensional space. Munsell’s 

system, and particularly the later renotations, is based 

on rigorous measurements of human subjects’ visual 

responses to color, putting it on a firm experimental 

scientific basis. Because of this basis in human 

visual perception, Munsell’s system has outlasted its 

contemporary color models, and though it has been 

superseded for some uses by models such as CIELAB 

(L*a*b*) and CIECAM02, it is still in wide use today. 

Explanation 

The system consists of three independent dimensions 

which can be represented cylindrically in three 

dimensions as an irregular color solid: hue, measured 

by degrees around horizontal circles; chroma, measured 

radially outward from the neutral (gray) vertical axis; and 

value, measured vertically from 0 (black) to 10 (white). 

Munsell determined the spacing of colors along these 

dimensions by taking measurements of human visual 

responses. In each dimension, Munsell colors are as 

close to perceptually uniform as he could make them, 

which makes the resulting shape quite irregular. As 

Munsell explains: 

Desire to fit a chosen contour, such as the pyramid, 

cone, cylinder or cube, coupled with a lack of proper 

tests, has led to many distorted statements of color 

relations, and it becomes evident, when physical 

measurement of pigment values and chromas is studied, 

that no regular contour will serve. 

—Albert H. Munsell, “A Pigment Color System and 

Notation” 

Hue 

Each horizontal circle Munsell divided into five principal 

hues: Red, Yellow, Green, Blue, and Purple, along with 5 

intermediate hues halfway between adjacent principal 

hues. Each of these 10 steps is then broken into 10 substeps, 

so that 100 hues are given integer values. Two 

colors of equal value and chroma, on opposite sides of a 

hue circle, are complementary colors, and mix additively 

to the neutral gray of the same value. The diagram 

below shows 40 evenly-spaced Munsell hues, with 

complements vertically aligned. 

Value 

Value, or lightness, varies vertically along the color solid, 

from black (value 0) at the bottom, to white (value 10) 

at the top.[5] Neutral grays lie along the vertical axis 

between black and white. 

Several color solids before Munsell’s plotted luminosity 

from black on the bottom to white on the top, with 

a gray gradient between them, but these systems 

neglected to keep perceptual lightness constant across 

horizontal slices. Instead, they plotted fully-saturated 

yellow (light), and fully saturated blue and purple (dark) 

along the equator. 

Chroma 

Chroma, measured radially from the center of each slice, 

represents the “purity” of a color, with lower chroma 

being less pure (more washed out, as in pastels).[6] 

Note that there is no intrinsic upper limit to chroma. 

Different areas of the color space have different maximal 

chroma coordinates. For instance light yellow colors 

have considerably more potential chroma than light 

purples, due to the nature of the eye and the physics 

of color stimuli. This led to a wide range of possible 

chroma levels—up to the high 30s for some hue–value

combinations (though it is difficult or impossible to make 

physical objects in colors of such high chromas, and they 

cannot be reproduced on current computer displays). 

Vivid soil colors are in the range of approximately 8. 

A color is fully specified by listing the three numbers for 

hue, value, and chroma. For instance, a fairly saturated 

purple of medium lightness would be 5P 5/10 with 5P 

meaning the color in the middle of the purple hue band, 

5/ meaning medium lightness, and a chroma of 10 (see 

the swatch to the right). 

History and Influence 

Several editions of the Munsell Book of Color. The atlas 

is arranged into a removable page of removable color 

swatches of varying value and chroma for each of 40 

particular hues. 

The idea of using a three-dimensional color solid to 

represent all colors was developed during the 18th 

and 19th centuries. Several different shapes for such 

a solid were proposed, including: a double triangular 

pyramid by Tobias Mayer in 1758, a single triangular 

pyramid by Johann Heinrich Lambert in 1772, a sphere 

by Philipp Otto Runge in 1810, a hemisphere by Michel 

Eugène Chevreul in 1839, a cone by Hermann von 

Helmholtz in 1860, a tilted cube by William Benson in 

1868, and a slanted double cone by August Kirschmann 

in 1895.[7] These systems became progressively more 

sophisticated, with Kirschmann’s even recognizing the 

difference in value between bright colors of different 

hues. But all of them remained either purely theoretical 

or encountered practical problems in accommodating 

all colors. Furthermore, none was based on any rigorous 

scientific measurement of human vision; before Munsell, 

the relationship between hue, value, and chroma was 

not understood. 

Professor Munsell, an artist, wanted to create a “rational 

way to describe color” that would use decimal notation 

instead of color names (which he felt were “foolish” and 


“misleading”), which he could use to teach his students 

about color. He first started work on the system in 1898 

and published it in full form in A Color Notation in 1905. 

The original embodiment of the system (the 1905 Atlas) 

had some deficiencies as a physical representation 

of the theoretical system. These were improved 

significantly in the 1929 Munsell Book of Color and 

through an extensive series of experiments carried out 

by the Optical Society of America in the 1940s resulting 

in the notations (sample definitions) for the modern 

Munsell Book of Color. Though several replacements 

for the Munsell system have been invented, building 

on Munsell’s foundational ideas—including the Optical 

Society of America’s Uniform Color Scales, and the 

International Commission on Illumination’s CIELAB 

(L*a*b*) and CIECAM02 color models—the Munsell 

system is still widely used, by, among others, ANSI to 

define skin and hair colors for forensic pathology, the 

USGS for matching soil colors, in Prosthodontics during 

the selection of shades for dental restorations, and 

breweries for matching beer colors. 

Reference: Wikipedia, 2010. Munsell colour system. [online] Available at: [Accessed 17/09/10].


Colour Language Versus Numbers – Munsell Colour System 

Hue Symbol 

Red R 

Yellow-Red YR 

Yellow Y 

Green-Yellow GY 

Green G 

Hue Symbol 

Blue-Green BG 

Blue B 

Purple-Blue PB 

Purple P 

Red-Purple RP


Reference: Defining Color, systems for precise color validation, 2007. [online] Available at: [Accessed 15/04/11].


Colour Language Versus Numbers – CIE L*a*b* 

Background 

The Hunter L, a, b color scale evolved during the 1950s 

and 1960s. At that time, many of the scientists involved 

with color measurement were working on uniform 

color scales. The XYZ system was being used, but it 

did not give a good indication of sample color based 

solely on the numbers. The uniform color scales being 

investigated gave better indications of the color of 

a sample based solely on the numbers. There were 

several permutations of the Hunter L, a, b color scale 

before the current formulas were released in 1966. 

The Hunter L, a, b color scale is more visually uniform 

than the XYZ color scale. In a uniform color scale, the 

differences between points plotted in the color space 

correspond to visual differences between the colors 

plotted. The Hunter L, a, b color space is organized 

in a cube form. The L axis runs from top to bottom. 

The maximum for L is 100, which would be a perfect 

reflecting diffuser. The minimum for L would be zero, 

which would be black. The a and b axes have no 

specific numerical limits. Positive a is red. Negative a is 

green. Positive b is yellow. Negative b is blue. Below is a 

diagram of the Hunter L, a, b color space. 

There are delta values (ΔL, Δa, and Δb) associated with 

this color scale. These values indicate how much a 

standard and sample differ from one another in L, a, and 

b. The ΔL, Δa, and Δb values are often used for quality 

control or formula adjustment. Tolerances may be set for 

the delta values. Delta values that are out of tolerance 

indicate that there is too much difference between 

the standard and the sample. The type of correction 

needed may be determined by which delta value is 

out of tolerance. For example, if Δa is out of tolerance, 

the redness/greenness needs to be adjusted. Whether 

the sample is redder or greener than the standard is 

indicated by the sign of the delta value. For example, if 

Δa is positive, the sample is redder than the standard. 

The total color difference, ΔE, may also be calculated. 

ΔE is a single value that takes into account the 

differences between the L, a, and b of the sample and 

standard. It does not indicate which parameter is out 

of tolerance if ΔE is out of tolerance. It may also be 

misleading in some cases where ΔL, Δa, or Δb is out of 

tolerance, but ΔE is still within the tolerance. 

The Hunter L, a, b color scale may be used on any 

object whose color may be measured. It is not used as 

frequently today as it was in the past because the CIE 

L*a*b* scale, which was released in 1976, has gained 

popularity. 

Reference: Hunterlab, 2008. Insight on color. [online] Available at: [Accessed 30/05/11].

Colour Language Versus Numbers – E Numbers 

E numbers are number codes for food additives that 

have been assessed for use within the European 

Union (the “E” prefix stands for “Europe”).[1] They 

are commonly found on food labels throughout the 

European Union.[2] Safety assessment and approval 

are the responsibility of the European Food Safety 

Authority.[3] The numbering scheme follows that of the 

International Numbering System (INS) as determined 

by the Codex Alimentarius committee[4] though only 

a subset of the INS additives are approved for use in 

the European Union. E numbers are also encountered 

on food labelling in other jurisdictions, including the 

GCC, Australia, New Zealand and Israel. The “E” 

prefix is omitted in Australia and New Zealand. They 

are increasingly, though still rarely, found on North 

American packaging, especially in Canada on imported 

European products. 

In casual language in the UK and Ireland, “E number” 

is used as a pejorative term for artificial food additives, 

and products may promote themselves as “free of E 

numbers” even though most of the natural ingredients 

contain components that also have an E number such 

as vitamin C (E300) or lycopene (E160d). This is because 

vitamin C has an E number (actually several E numbers, 

300-305, for different chemical forms of the vitamin), it 

is impossible to live off a diet without any substances 

that have E numbers. “Free of E numbers” then simply 

means that pure forms of the substances are not 

intentionally added, even though identical substances 

certainly exist naturally in many foods. 


Reference: Wikipedia, 2010. E number. [online] Available at: [Accessed 17/09/10].


Colour Language Versus Numbers – E Numbers 

Code Name Purpose Status 

E100 Curcumin, turmeric food colouring (yellow-orange) N/A 

E101 Riboflavin (Vitamin B2), formerly called lactoflavin 

(Vitamin G) 

food colouring (yellow-orange) N/A 

E101a Riboflavin-5’-Phosphate food colouring (yellow-orange) N/A 

E102 Tartrazine (FD&C Yellow 5) food colouring (lemon yellow) Unpermitted 

E103 Chrysoine resorcinol food colouring (golden) Forbidden 

E104 Quinoline Yellow WS food colouring (dull or greenish yellow) Undergoing a voluntary phase-out in the UK. 

E105 Fast Yellow AB food colouring (yellow) N/A 

E106 Riboflavin-5-Sodium Phosphate food colouring (yellow) N/A 

E107 Yellow 2G food colouring (yellow) N/A 

E110 Sunset Yellow FCF (Orange Yellow S, FD&C 

Yellow 6) 

food colouring (yellow-orange) Banned in Finland, Norway & the UK (voluntarily). 

Products in the EU require warnings and is evaluating 

a phase-out. 

E111 Orange GGN food colouring (orange) N/A 

E120 Cochineal, Carminic acid (carmines, Natural 

Red 4) 

food colouring (crimson) N/A 

E121 Citrus Red 2 food colouring (dark red) Forbidden 

E122 Carmoisine, Azorubine food colouring (red to maroon) Undergoing a voluntary phase-out in the UK, currently 

banned in Canada, Japan, Norway, USA and 

Sweden. EU currently evaluating health risks. 

E123 Amaranth (FD&C Red 2) food colouring (dark red) Forbidden 

E124 Ponceau 4R (Cochineal Red A, Brilliant Scarlet 

4R) 

food colouring (red) Unpermitted 

E125 Ponceau SX, Scarlet GN food colouring (red) N/A 

E126 Ponceau 6R food colouring (red) N/A 

E127 Erythrosine (FD&C Red 3) food colouring (red) Unpermitted 

E128 Red 2G food colouring (red) Forbidden 

E129 Allura Red AC (FD&C Red 40) food colouring (red) Banned in Denmark, Belgium, France, Switzerland 

and Sweden. Undergoing a voluntary phase out in 

the UK. Permitted in the USA and by the EU (whilst 

preserving individual country bans). 

E130 Indanthrene blue RS food colouring (blue) N/A 

E131 Patent Blue V food colouring (dark blue) 

E132 Indigo carmine (indigotine, FD&C Blue 2) food colouring (indigo) 

E133 Brilliant Blue FCF (FD&C Blue 1) food colouring (reddish blue) N/A 

E140 Chlorophylls and Chlorophyllins: (i) Chlorophylls 

(ii) Chlorophyllins 

E141 Copper complexes of chlorophylls and chlorophyllins 

(i) Copper complexes of chlorophylls (ii) 

Copper complexes of chlorophyllins 

food colouring (green) N/A 

food colouring (green) N/A 

E142 Green S food colouring (green) Unpermitted 

E143 Fast Green FCF (FD&C Green 3) food colouring (sea green) N/A 

E150a Plain caramel food colouring N/A 

E150b Caustic sulfite caramel food colouring N/A

E150c Ammonia caramel food colouring N/A 

E150d Sulphite ammonia caramel food colouring N/A 

E151 Black PN, Brilliant Black BN food colouring N/A 

E152 Black 7984 food colouring N/A 

E153 Carbon black, Vegetable carbon food colouring N/A 

E154 Brown FK (kipper brown) food colouring Unpermitted 

E155 Brown HT (chocolate brown HT) food colouring N/A 

E160a Alpha-carotene, Beta-carotene, Gammacarotene 

E160b Annatto, bixin, norbixin food colouring 

food colouring N/A 

E160c Paprika oleoresin, Capsanthin, capsorubin food colouring N/A 

E160d Lycopene food colouring N/A 

E160e Beta-apo-8’-carotenal (C 30) food colouring N/A 

E160f Ethyl ester of beta-apo-8’-carotenic acid (C 30) food colouring N/A 

E161a Flavoxanthin food colouring N/A 

E161b Lutein food colouring N/A 

E161c Cryptoxanthin food colouring N/A 

E161d Rubixanthin food colouring N/A 

E161e Violaxanthin food colouring N/A 

E161f Rhodoxanthin food colouring N/A 

E161g Canthaxanthin food colouring N/A 

E161h Zeaxanthin food colouring N/A 

E161i Citranaxanthin food colouring N/A 

E161j Astaxanthin food colouring N/A 

E162 Beetroot Red, Betanin food colouring N/A 

E163 Anthocyanins food colouring N/A 

E170 Calcium carbonate, Chalk food colouring N/A 

E171 Titanium dioxide food colouring (pure white) N/A 

E172 Iron oxides and hydroxides food colouring N/A 

E173 Aluminium food colouring Unpermitted 

E174 Silver food colouring N/A 

E175 Gold food colouring N/A 

E180 Pigment Rubine, Lithol Rubine BK food colouring Unpermitted 

E181 Tannin food colouring N/A 

E182 Orcein, Orchil food colouring N/A 


Reference: Wikipedia, 2010. E number. [online] Available at: [Accessed 17/09/10].

Colour 

Terms

Basic Colour Terms 

this, as it was originally a word that referred to a dye 

(see Tyrian purple). 

The word “orange” is also difficult to categorize as 

abstract or descriptive because both its use as a color 

word and as a word for an object are very common 

and it is difficult to distinguish which is the primary 

and which is the secondary use of the word. As a basic 

color term it became established in the early to mid 

20th century; before that time artist’s palettes called 

it “yellow-red”. On the one hand the fruit “orange” 

has the color “orange,” and etymologically the word 

“orange” as a fruit (from the Sanskrit “narang” or Tamil 

“naraththai” via the Portuguese “laranja”) preceded the 

use of “orange” as a color word in English. On the other 

hand “orange” (color) is usually given equal status to 

red, yellow, green, blue, purple, brown, pink, gray, white 

and black (all abstract colors) in membership to the 

‘basic color terms’ of English; the derived form orangish 

is attested from the late 19th century.[5] Based solely 

on current usages of the word it would be impossible 

to distinguish if an orange is called an orange because 

the fruit is orange, or if the color orange is called orange 

because oranges are orange [6] (other examples of this 

problem are the colors “violet” and “indigo”). 

Recently, a researcher at Hewlett-Packard, Nathan 

Moroney, has been performing an online experiment 

in unconstrained color naming in English and 21 other 

languages. He has published[9] some of the results of 

this work and the experiment is ongoing. 

Standardized Systems 

Some examples of color naming systems are CNS and 

ISCC–NBS lexicon of color terms. The disadvantage of 

these systems, however, is that they only specify specific 

color samples, so while it is possible to, by interpolating, 

convert any color to or from one of these systems, a 

lookup table is required. In other words, no simple 

invertible equation can convert between CIE XYZ and 

one of these systems. 

Philatelists traditionally use names to identify postage 

stamp colors. While the names are largely standardized 

within each country, there is no broader agreement, 

and so for instance the US-published Scott catalog will 

use different names than the British Stanley Gibbons 

catalogue. 

On modern computer systems a standard set of basic 

color terms is now used across the web color names 

(SVG 1.0/CSS3), HTML color names, X11 color names 

and the .NET Framework color names, with only a few 

minor differences. 

The Crayola company is famous for its many crayon 

colors, often creatively named. 

Color Names, Paint Stores, and Fashion 

Color naming in fashion and paint exploits the 

subjectiveness and emotional context of words and 

their associations. This is particularly seen in the naming 

of paint chips and samples where paint is sold. This 

may in fact be an aid to moving a customer through 

the store more rapidly, as closely similar shades may 

be equally valid in a specific application, with selection 

being determined by individual preference, colors of 

furnishings and artwork, and the quality and character 

of light, both artificial and natural. The attachment of an 

emotional context to a color sample by choice of name 

may enhance the rapidity of selection. 

In fashion and automotive colors the objective of 

naming is to enhance the perception of color through 

appropriate naming to fit the emotional context desired. 

Thus the same “poppy yellow” can become either 

the hot blooded and active “amber rage”, the cozy 

and peaceful “late afternoon sunshine”, or the wealth 

evoking “sierra gold”. The divisions of General Motors 

often give different names to the same colors. 

Names given to the most vivid colors often include 

the word neon, alluding to the bright glow of a neon 

light. Dyes and inks producing these colors are often 

fluorescent, producing a luminous glow when viewed 

under a black light. 

Reference: Wikipedia, 2010. Colour Term. [online] Available at: [Accessed 17/09/10].

Basic Colour Terms, Their Universality and Evolution – Wikipedia 

Basic Color Terms: Their Universality and Evolution 

(1969) (ISBN 1-57586-162-3) is a book by Brent Berlin and 

Paul Kay. Berlin and Kay’s work proposed that the kinds 

of basic color terms a culture has, such as black, brown 

or red, are predictable by the number of color terms the 

culture has. 

Berlin and Kay posit seven levels in which cultures fall, 

with Stage I languages having only the colors black 

(dark–cool) and white (light–warm). Languages in Stage 

VII have eight or more basic color terms. This includes 

English, which has eleven basic color terms. The authors 

theorize that as languages evolve, they acquire new 

basic color terms in a strict chronological sequence; 

if a basic color term is found in a language, then the 

colors of all earlier stages should also be present. The 

sequence is as follows: 

Stage I: Dark-cool and light-warm (this covers a 

larger set of colors than English “black” and 

“white”.) 

Stage II: Red 

Stage III: Either green or yellow 

Stage IV: Both green and yellow 

Stage V: Blue 

Stage VI: Brown 

Stage VII: Purple, pink, orange, or grey 

The work has achieved widespread influence. However, 

the constraints in color-term ordering have been 

substantially loosened, both by Berlin and Kay in later 

publications, and by various critics. Barbara Saunders 

questioned the methodologies of data collection and 

the cultural assumptions underpinning the research, as 

has Stephen C. Levinson. 


Reference: Wikipedia, 2010. Basic Color Terms: Their Universality and Evolution. [online] Available at: 


Colour Names 

The Colour of Words – The Fugitive Names of Hues by Michael Quinion 

Words for colours are slippery things. This came 

particularly to mind when I was reading through some 

fashion pages the other day as part of my eternal 

search for new vocabulary. One piece of clothing, 

whose coloured illustration showed it to be a sort of 

dull pastel green, was described as being khaki in 

colour. Now I am old enough to remember the colour of 

British army uniforms just after the Second World War. 

Their uniforms were also said to be khaki (so much so 

that to be in khaki meant to be in the Army), but they 

were most certainly also a sandy brown with no hint of 

green. This fitted the etymology of the word — a legacy 

of British rule in India — which comes from an Urdu 

word meaning “dusty” (no connection with the ancient 

informal English term cacky, though the implied colours 

are, or were, similar). 

If a word so recent and apparently so clearly defined 

can change meaning, almost without anyone noticing, 

perhaps it is not so surprising that other colour words 

have done the same through history, even those for the 

primary colours that you would think too well-grounded 

in nature to suffer much change. 

Take yellow for example. This has been traced to an 

Indo-European root *ghel or *gohl which seems to have 

denoted both yellow and green. This has evolved into 

many terms which have reached English by a variety of 

routes, including jaundice (from Latin galbus “greenishyellow”, 

via French), gold (so that “golden-yellow” is a 

tautology, etymologically speaking), choleric (from the 

Greek word for “bile”, which is yellow-green in colour) 

and yolk (which, therefore, just means “the yellow part 

of the egg”). The word blue has had an even more 

eventful history. It started out, apparently, as the Indo- 

European root *bhlewos, meaning “yellow”, and evolved 

into the Greek phalos, “white”, and hence in Old English 

to “pale” and “the colour of bruised skin”; we actually 

re-borrowed the word blue in its modern sense from 

French. However, the word green seems always to 

have been tightly bound to the idea of growing things: 

indeed green and grow come from the same Germanic 

root. Red is another colour-fast word, related to the 

Greek eruthros (hence words like erythrocyte, “red 

blood cell”) and to the English words russet, ruby, ruddy 

and rust. 

In another colour transition, the hair colour auburn 

once meant “brownish-white” or “yellowish-white” (it 

derives from Latin albus, “white”, via the medieval Latin 

alburnus, “whitish; off-white”) and only shifted sense 

to refer to a shade of brown in the sixteenth century, 

seemingly because it was sometimes spelled “abrun” 

or “a-brown” and was misunderstood as deriving from 

“brown”. Though some older dictionary definitions say it 

could mean either “golden-brown” or “reddish-brown”, 

the sense has continued to shift so that now it refers 

exclusively to the latter colour. 

The word pink is generally agreed to be derived from 

the similar Dutch word pinck. However, there are two 

theories about which sense of the Dutch word was 

involved, and how it became applied to the colour. 

One is that it came from pinck in the sense of “small” 

(which turns up in the modern English word pinky for 

“little finger”), through the expression pinck oogen 

“small eyes” — that is, “half-closed eyes” — and that 

this was borrowed into English and applied to the 

flowers of the common English cottage-garden species 

Dianthus plumarius, which has been called a pink since 

the seventeenth century. The other theory says it came 

from pinck in the sense of “hole” (which is the origin 

of pinking shears, the device used to make ornamental 

holes in cloth) and was applied to the flowers of 

Dianthus because they resembled the shape of the 

holes. Either way, the colour comes from the plant, not 

the other way round. 

Many other modern colour words are similarly derived 

from the colours of plants and natural substances, which 

have long been raided by colourists in search for names 

to apply to the ever-more subtle shades which turn up 

in commercial colour charts. There’s no great surprise 

in colours like cinnamon, tangerine, oyster, lime, melon,

glacier, apple white, ivory, silver, chocolate, amber or 

aubergine, though there probably is in puce, a colour 

which seems intrinsically comic even if you don’t know 

that it actually means “flea coloured” (from Latin pulex 

via French). 

Quite a large set of our less-common colour words have 

similarly come from French: the currently-fashionable 

shade taupe for a brownish-grey colour comes from the 

word for mole; an earlier fashion gave us greige, at first 

spelled grège, which shows that it was borrowed from 

the French word for the colour of raw silk; beige is a 

transferred epithet from the French name for a type of 

woollen fabric usually left undyed; ecru similarly comes 

from the French écru, “unbleached”; and maroon is 

derived from the French name for the sweet chestnut, 

whose fruit is that distinctive brownish-red colour. 

Other colour names originate in those for precious 

stones: aquamarine, for example, was originally the 

name of a type of beryl (Latin aqua marina, “(the 

colour of) sea water”, referring presumably to the 

Mediterranean and not to the dull grey-green of British 

waters. Ultramarine might seem to be a directly-related 

word, as it refers to a deeper shade of blue, but the 

“ultra” part of it means “beyond” in the literal sense 

— a stone which came from across or beyond the sea, 

since it was made from ground-up lapis lazuli imported 

from Asia (a much-modified version of the Arabic name 

for the mineral gave rise to azure in medieval English). 

The word turquoise comes from the Old French pierre 

turquoise, the “Turkish stone”, though the word is 

now used more frequently in its colour sense than in 

reference to the stone, unlike emerald, which retains 

both its literal and figurative senses in about equal 

measure. 

The colour orange derives originally from the Sanskrit 

word narangah for the fruit, whose name moved 

westwards through Persian narang and Arabic naranj to 

Spanish (the Arabs imported it into Europe via Moorish 

Spain in medieval times); in French it became corrupted 


to orange, in part by the process called metanalysis but 

also through being strongly influenced by the name of 

the town of Orange in south-eastern France which used 

to be a centre of the orange trade. 

Purple comes to us from Greek via Latin and refers to 

the dye extracted from a species of Mediterranean 

shellfish, which was so rare and valuable that it was 

reserved for royal garments. However, the colour from 

the dye is very variable, and could at times be crimson 

(the colour of cardinals’ robes), well removed from the 

colour we normally associate with the word. When 

William Perkin discovered his first synthetic dyestuff in 

1856, derived from aniline, he called it at first aniline 

purple but subsequently changed its name to the more 

distinctive mauve, taken from the French word for 

the mallow plant, whose stems were purple (his new 

dye became so popular that the 1860s were called 

the mauve decade). It’s good he changed the name, 

because the chemical compound aniline that was a 

starting point for the new dye was so named in 1841 

after anil, the common word at one time for the purple 

vegetable dye we now call indigo (from the Portuguese 

word which means “the Indian (dye)” because that was 

where they got it from), so aniline purple is very nearly a 

tautology. 

The word crimson I’ve just used comes from the Sanskrit 

krmi-ja, “(a dye) produced from a worm” (actually it 

came from the dried bodies of a small insect), through 

the Arabic qirmaz and the Old Spanish cremesin (via 

medieval Latin this also gave us carmine); the insect was 

called the kermes but a continuing mistaken belief that 

it was a worm also gave rise to the word vermillion (Latin 

“worm-coloured”, from vermiculus, the Latin term for 

the kermes). Yet another word for this colour, scarlet, 

was not originally a colour word at all, but referred to a 

high-quality cloth which may have originated in Persia, 

and which could have been blue or green, though it 

was commonly dyed red. So Will Scarlet of Robin Hood 

legend may just have been well-dressed.

Colour Names 

Magenta, a key colour in photographic reproduction, 

derives its name from a dye discovered by the London 

company Simpson, Nicholson and Maule. In 1859, 

Edward Nicholson found a way to make it from aniline, 

and marketed it under the name magenta after 

Garibaldi’s then-recent victory in northern Italy. (The 

chemical name of the dye is fuchsine, named by E 

Verguin — who discovered another way of making it in 

France at almost the same time — after the purple-red 

flowers of the fuchsia plant, which itself commemorates 

the sixteenth-century German botanist Leonhard Fuchs, 

though delicately re-pronounced to spare the blushes of 

the innocent.) 

The word livid which turned up earlier has an odd 

history, which may be guessed from the entry in one 

of my etymological dictionaries which said “livid: see 

sloe”. The connection is that the word sloe probably 

originally meant the “blue-black” fruit, perhaps being 

derived from an ancient Germanic form *slaikhwon, 

which may be linked with the Latin livere, “(be) blueblack”. 

It became applied to the similar colour of 

bruises when it was first introduced in the seventeenth 

century (similar in sense to the idiom black and blue). 

But — perhaps because the colour of bruises is so 

variable — its sense shifted about in a confusing manner 

until any firm connection with a single colour was lost. 

As an illustration of this, my Roget’s Thesaurus gives 

five references for the word in its index: “blackish”, 

“grey”, “colourless”, “purple” and “angry”. This shift of 

associations may have come about because the word 

was applied to the colour of death, say in phrases like 

the livid lips of the corpse, in which the word means 

“ashen”, or “leaden”. It may then have become linked 

to the colour of the complexion during rage, in which 

the face can go a lifeless colour through blood draining 

from the skin (in fact, the Oxford English Dictionary 

defines the word to mean “(of) a bluish leaden colour” 

and says firmly that it was applied to complexions 

because enraged people went pale with emotion). My 

guess is that the word became so strongly attached to 

this figurative sense of “enraged” that it was mistakenly 

re-applied to the flushed, purplish colour which is even 

more common when someone is angry. What is certain 

is that the only safe way to use livid these days is to 

avoid colour associations and use only its figurative 

sense of “enraged”: 

Trying to keep track of these shifting colour names can 

make you absolutely livid 

Reference: QUINION, M., 1996. The fugitive names of hues. [online] Available at: [Accessed 20/09/10].

Fictional Colors – Wikipedia 

Fuligin – both a color and a textile having that color, 

associated with the Guild of Torturers in Gene Wolfe’s 

book, The Shadow of the Torturer. The color is defined 

as “the color that is darker than black” and also as “the 

color of soot.” (The noun is a back-formation from the 

adjective fuliginous, “sooty.”) 

Grue and bleen – colors that change after an arbitrary, 

but fixed time; coined by Charles Dodgson[citation 

needed] and used by philosopher Nelson Goodman to 

illustrate what he calls “the new riddle of induction.” 

Hooloovoo – a superintelligent shade of the color 

blue in The Hitchhiker’s Guide to the Galaxy series by 

Douglas Adams. 

Octarine – the color of magic in the Discworld fantasy 

novels, described as resembling a fluorescent greenishyellow 

purple. 

Optic White – a very bright white that is created by 

added drops of black paint in “Invisible Man” by Ralph 

Ellison. 

Squant – a fourth primary color publicized by the 

experimental band Negativland in 1993. 

Jale and Ulfire – new primary colors (shades of 

ultraviolet?) in A Voyage to Arcturus by David Lindsay. 

The Colour Out of Space – a vaguely-described alien 

hue, from the story of that name by H. P. Lovecraft. 

The colors tang and burn are colors in the infrared 

range seen by the albino mutant Olivia Presteign (whose 

vision only functions in the infrared) in the 1956 science 

fiction novel The Stars My Destination by Alfred Bester. 

Htun is a color similar to black only seen by gnomes in 

the book Fairest by Gail Carson Levine. 

Reference: Wikipedia, 2010. Fictional Colors. [online] Available at: [Accessed 09/08/10]. 


Colour Naming 

How Colourful Language Can Improve Your Image by David Bradley 

Colourful language usually refers euphemistically to the 

kind of expletives and oaths you hear in a barrack room 

brawl. But, in the context of technology it could be the 

next big thing in colour printing. 

Colour and natural language experts at Xerox have been 

working on what sounds like an entirely new way to get 

the best out of your digital photos. Their research could 

allow you to talk to your printer and tell it to “make the 

green a ‘mossy’ green” or “make the sky more sky blue”. 

More technically, you might one day be able to do all 

the kinds of colour and contrast corrections that are 

usually the preserve of programs like Photoshop, with 

simple phrases sent to the printer itself. 

The approach speed up the workflow for graphic artists, 

printers, photographers and other image professionals 

and their assistants who could save time side-stepping 

the on-screen fine tuning process of printouts. 

“You shouldn’t have to be a colour expert to make the 

sky a deeper blue or add a bit of yellow to a sunset,” 

research leader Geoff Wolfe says. The software is still 

in the development stages, but works by translating 

human descriptions of colour – “emerald green”, “brick 

red”, “sky-blue pink” – into the precise numerical codes 

printers use to control the amount of each primary 

colour they deposit at a single point in the printed 

image. 

“Today, especially in the office environment, there are 

many non-experts who know how they would like colour 

to appear but have no idea how to manipulate the color 

to get what they want,” Woolfe adds. Moreover, the vast 

majority of computer screens in “non-expert” offices are 

setup incorrectly for screen to print comparisons and so 

cause the whole gamut of problems when a document 

that looks okay on screen is printed. Simple commands 

to rectify such issues avoid the problem of having to 

know how to set up the screen and ambient lighting. 

Woolfe’s discovery could mean that colour adjustments 

can be made on devices like office printers and 

commercial presses without having to deal with the 

mathematics. For instance, cardinal red on a printer or 

monitor is really expressed by a set of mathematical 

coordinates that identify a specific region in a threedimensional 

space, which is the gamut of all the colours 

that the device can display or print. To make that colour 

less orange, the colour expert distorts (morphs) that 

region to a new region in the gamut. 

The ability to use common words to do this gamut 

morphing and adjust colour would have far-reaching 

implications for non-experts as well as graphic artists, 

printers, photographers and other professionals who 

spend a significant amount of time fine tuning the 

colours in documents. 

“In the end it’s all about usability,” Woolfe adds, 

“Colour is so prevalent today, you shouldn’t have to be 

an expert to handle it.” 

Reference: BRADLEY, D., 2007. How colourful language can improve your image. Science Base, [blog] 30 April Available at: [Accessed 11/10/10].

Colour Names 

Artist’s Pigments – Names and Origins 

Alizarin Crimson 

Alizarin Crimson is the synthetic version of the pigment 

found in Madder plants. It was first synthesized in 1868 

by the German chemists, Grabe and Lieberman, as a 

more lightfast substitute to Rose Madder. Madder lakes, 

which were produced in a variety of shades of red, 

from brownish to purplish to bluish, made good glazing 

colours that spread well in oil, and were also prepared 

in a form for use in watercolour painting. However, 

some painters found that the synthetic variety was less 

saturated and brilliant than natural Madder. Moreover, 

late 20th-century tests revealed that Alizarin Crimson 

pigment was much less lightfast than its natural parent. 

Antimony Vermilion 

A brightly coloured, lightfast pigment whose reputation 

suffered in the mid-19th century as it reacts with lead 

pigments and turns black. Now obsolete. 

Antwerp Blue 

A variant of Prussian Blue, containing 75 percent 

extender. Not a reliable pigment. Now obsolete. 

Asphaltum 

Asphaltum comprises a solution of asphalt in oil or 

turpentine, which has been employed since Antiquity, 

if not earlier, as a protective coating. Rembrandt, for 

instance, is said to have used Asphaltum successfully in 

a number of his paintings. It was later used to give an 

“Old Master” look to canvases. Unfortunately, in some 

cases it caused noticeable darkening and cracking. It 

persisted as a pigment until the end of the 19th century. 

Now obsolete. 

Atramentum (Atramentum Librarium) 

An old generic type of term referring to the colour of ink 

- mainly blacks, but also reds, greens, and violets which 

were the traditional colours used by classical artists and 

calligraphers. 

Aureolin 

Also known as Cobalt Yellow, Aureolin superceded 

Gamboge, an earlier pigment which was an Asian yellow 

gum in used until the 19th century. Aureolin - an intense 

medium yellow pigment - was synthesized in 1848 by 

N.W. Fischer in Germany, and was employed in oil and 

watercolour painting until the late 19th century, when 

less expensive, and more lightfast pigments (eg. the 

Cadmiums) were introduced. 

Azurite 

A greenish blue pigment named after the Persian word 

“lazhward” meaning “blue”, it is chemically close to 

the green colourant malachite. Azurite was known from 

Ancient times and became extremely popular during 

the Middle Ages and Renaissance era, as Egyptian 

Blue declined. Used in oil painting, it performed best 

as a water-based pigment and was often employed 

in Tempera paint under an oil glaze. Superceded by 

Prussian blue in the early 18th century, and rendered 

obsolete after the synthesisation of Ultramarine and the 

development of Cobalt Blue. 

Barium Yellow 

A relatively opaque white-yellow pigment, it is a form 

of Barium Chromate, and is also known as Lemon 

Yellow. Permanent in most media, it performed best in 

watercolour paints. Now obsolete. 

Bismuth White 

Developed in the early 19th century it was replaced 

by Zinc White by the 1830s. It had the advantage of 

being much less toxic than many other colours, but it 

was prone to darkening when combined with pigments 

containing sulfur. 

Bistre 

An unreliable brown pigment made by burning Beech 

wood. Now obsolete. 

Black 

See Carbon Black (below). 

Bole 

A form of natural red iron oxide. The closest modern 

pigment to Bole would be light red in colour. Now 

obsolete.

Bone White 

Obsolete; it was made by burning bones to a white ash. 

Cennino Cennini in his Il Libro dell’Arte says ‘the best 

bones are from the second joints and wings of fowls and 

capons; the older they are, the better; put them into the 

fire just as you find them under the table.’ It was used as 

a ground for panels. 

Bremen Blue 

A synthetic copper blue pigment without the 

permanency of Azurite. It was manufactured in 

numerous shades and had many common names. 

Used until the early 20th century, mainly because of its 

attractive hue. 

Burnt Carmine 

A fugitive dark red type of Carmine but less permanent. 

After roasting it was typically mixed with Van Dyke 

Brown to obtain the richest shades. Now obsolete. 

Burnt Sienna 

An iron oxide pigment, coloured a warm mid-brown. 

Made by burning raw sienna (Terra di Sienna). 

Burnt Umber 

See Umber (below). 

Cadmium Pigments 

A family of pigments based on the metal cadmium, 

in hues of yellow, orange and red. Cadmium yellow 

is cadmium sulfide, to which increasing amounts of 

selenium may be added to extend the colour-range. 

Viridian is added to Cadmium yellow to produce 

the bright, pale green pigment cadmium green. The 

brightness of Cadmium colours tends to fade in murals 

and fresco painting. Although Cadmium was discovered 

by Stromeyer in 1817, production of pigments was 

delayed until after 1840 due to scarcity of the metal. All 

of the cadmiums possessed great colour brilliance with 

the deeper shades having the greatest tinting strength. 

Cadmium pigments were used in both oil painting and 

watercolour but could not be combined with copperbased 

pigments. 

Cadmium Orange 

See Cadmium Pigments (above). 

Cadmium Yellow 

See Cadmium Pigments (above). 


Carbon Black 

An ancient black pigment, it was traditionally made by 

charring organic materials like wood or bone. It was a 

pure form of carbon, and was referred to by a variety of 

names, depending on how it was made. For example: 

“Ivory black” was produced by burning ivory or bones; 

“Vine black” was made by charring dried grape vines; 

“Lamp black” was made from soot collected from oil 

lamps. Synthetic versions have now replaced these 

traditional organic forms, except in certain specialized 

arts, like calligraphy and Oriental painting. 

Carmine (Cochineal and Kermes) 

Used since Antiquity, Carmine is a natural organic 

crimson pigment/dye made from the dried bodies of the 

female insect Coccus cacti (Cochineal), which inhabits 

the prickly-pear cactus, and also from a wingless insect 

living on certain species of European live oaks (Kermes). 

The cactus insects were first heated in ovens, then dried 

in the sun, to produce “silver cochineal” from which 

the finest pigment was made. Cochineal is still made in 

Mexico and India. 

Celadon Green 

A variant of Green Earth pigment containing celadonite 

which gives it a greyish pale green colour. Most of these 

versions of Green Earths have been mined to exhaustion 

and are no longer easily available. 

Cerulean Blue 

Named after the Latin word “caeruleum” (meaning sky 

or heavens) which was used in classical times to describe 

various blue pigments, Cerulean is a highly stable 

and lightfast greenish-blue pigment, first developed 

in 1821 by Hopfner, but not widely available until its 

reintroduction in 1860 by George Rowney in England, 

as a paint-pigment for aquarelle watercolour art and 

oil painting. Although based on cobalt, it lacked the

Colour Names 

opacity and richness of cobalt blue. Even so, in oil, it 

maintained its colour better than any other blue and was 

especially popular with landscape painters for skies. 

Ceruse (Obsolete name for Lead White, Flake white, 

also Nottingham White) 

Basic lead carbonate. In use since the prehistoric Greek 

period, the second oldest artificially produced pigment. 

It was the only white oil-colour available to artists until 

the middle of the 19th century. 

Chrome Yellow, Chrome Red 

A family of inexpensive natural pigments made from 

lead chromate, first developed in about 1800 by the 

French chemist Louis Vauquelin, which became very 

popular (and a welcome alternative to both Turner’s 

Patent yellow and Orpiment) due to their opacity, their 

bright colours and low price. However, their tendency to 

darken over time, coupled with their lead content, has 

led to their replacement by the Cadmium family. 

Chrysocolla 

A natural green copper pigment first used by the 

Ancient Egyptians alongside Malachite. It was 

superceded by Egyptian Green. 

Cinnabar (Zinnober) 

This natural ore (Mercuric Sulfide) was a popular source 

for a red-orange artist-pigment also known as Vermilion. 

In fact the terms “cinnabar” and “vermilion” were used 

interchangeably to refer to either the natural or the later 

synthesized colour until around the 17th century when 

vermilion became the more common name. By the late 

18th century, the name cinnabar was applied only to 

the unground natural mineral. An opaque red pigment, 

Cinnabar production was dominated by the Chinese 

who found an early means of making it that remained 

the best method for over 1,000 years. Unfortunately, it is 

highly toxic. Most natural vermilion comes from cinnabar 

mines in China, hence its alternative name of China red. 

It was replaced by the Cadmium Reds during the 19th 

century. See also Vermilion (below). 

Cobalts 

A family of pigments originally derived from mineral 

mines in Bohemia. They were named Cobalt after 

the word “kobolds” - the Bohemian word for spirits 

or ghosts, which the miners believed inhabited the 

pigment and caused them difficulties. 

Cobalt Blue 

An expensive but highly stable pure blue pigment 

discovered by Thénard in 1802, it was a great 

improvement on smalt - the pigment made from cobalt 

blue glass. It is now the most important of all the cobalt 

pigments. Following the development of smalt by the 

Swedish chemist Brandt, and the German scientists 

Gahn and Wenzel, Louis Jaques Thénard discovered 

his new cobalt blue through experiments at the Sevres 

porcelain factory. It is totally stable in watercolour and 

fresco 

painting and a good substitute for ultramarine blue 

when painting skies. 

Cobalt Green 

A semi-transparent but highly permanent moderately 

bright green pigment discovered by the Swedish 

chemist Rinmann in 1780, it is used in all painting 

techniques. However its poor tinting strength and high 

cost of cobalt green has kept its use limited. 

Cobalt Violet 

Cobalt Violet was developed around 1860, and like its 

older sister Cobalt Green suffered from high cost and 

weak colouring power which restricted its use among 

artists. It has been superceded by the cleaner, stronger 

pigment Manganese Violet. 

Cobalt Yellow 

Discovered in 1848 in Breslau by the German scientist 

N W. Fischer, this pure yellow pigment was popular for 

a brief period due to its good mixing quality with other 

pigments and for good tints in watercolour. It is also 

lightfast. However, like most of the Cobalts, it is both 

expensive and of limited power.

Copper Resinate 

Known since the mid-Byzantine era (c.800 CE), this is a 

transparent jade-green glaze made by dissolving copper 

salts in Venice turpentine. It was used particularly by 

Post-Renaissance 16th-century Italian oil painters, to 

colour foliage. It was commonly combined with azurite 

paint, and layered over lead white or lead-tin yellow 

pigments. 

Cornflower Blue 

A blue dye made from the petals of the flower, and 

which was used by some water-colourists in the 18th 

century. 

Cremnitz White 

See White Lead (below). 

Dragon’s Blood 

A warm ruby-red resinous exudation of Calamus draco 

found in eastern Asia. Its use in Europe in painting dates 

back to the 1st century. Medieval illuminators employed 

it. Pliny the Elder expounded his fanciful idea that 

the substance was actually the mixed blood of those 

legendary enemies, the dragon and the elephant, which 

was spilt during their mortal combat. 

Egyptian Blue 

Also known as Egyptian Blue Frit, this dark blue pigment 

(calcium copper silicate) is arguably the first ever 

synthetic pigment, and arose out of the manufacture of 

dark blue glass by glass-makers in Ancient Egypt. The 

glass was ground into a deep permanent blue pigment 

of great visual beauty. It was used throughout antiquity 

as a blue pigment to colour a variety of differing 

mediums like stone, wood, plaster, papyrus, and canvas. 

Despite its relatively weak colouring power, it remained 

the only dark blue paint colour until the development 

of Ultramarine four Millennia later. In the 17th century 

an improvement to the original formula was developed 

known as Smalt (Alexandria Blue), which was used until 

the successful synthesis of Ultramarine in the 19th 

century. 

Egyptian Brown 

Another name for Mummy (see below). 


Egyptian Green 

A variant of Egyptian Blue (see above) that was 

developed in the later part of Ancient Egyptian times. It 

has similar properties to Egyptian Blue. Now obsolete. 

Emerald Green 

Also known as Schweinfurt Green, Parrot Green, 

Imperial Green, Vienna Green, and Mitis Green, this 

beautiful but poisonous of pigments was also marketed 

under the name Paris Green as a rat poison. As a paintpigment, 

it was prone to fading in sunlight (an effect 

which could be reduced in oil paintings by isolating the 

pigment in between coats of varnish) and also reacted 

chemically with other colours. For instance, it could 

not be combined with sulfur-containing colours, like 

cadmium yellow, vermilion or ultramarine blue, as the 

mixture resulted in a deep brown colour. However, it 

had a brilliance unlike any other copper green known 

to modern chemistry. It is said that Emerald Green 

was the favourite pigment of the Post-Impressionist 

Paul Cezanne. In some of his watercolours, thin washes 

containing the colour have browned, but thicker 

applications have remained bright green. Van Gogh was 

another avid user. Modern imitations include “Emerald 

Green” or “Permanent Green”. 

Folium 

A deep violet, sometimes bluish, or reddish colour made 

from Turnsole or Woad (see below), it was a general 

name for such colours employed by book illuminators 

and illustrators. The name stems from “folia” the latin 

word for pages in a book. 

French White 

A synonym for White Lead (see below). 

Fustic 

A yellow dye that is obtained from the plant 

Chlorophona tinctoria, native to the Americas,

Colour Names 

introduced to Europe in the 16th century. It had a 

limited use with water-colour. Older name was ffusticke 

yealowe. 

Gallstone 

Prepared from the gallstone of an ox and gives a 

reasonably dark yellow. Nicholas Hilliard found it useful 

for shading with miniature work. John Payne in the 

18th century found that dishonest colourmen were 

selling an inferior substitute. He suggested in his book 

on miniature-painting that artists should approach 

slaughter-houses and that the men there should be on 

the watch for gallstones. In 1801 it was one of the top 

four most expensive colours, Ackerman’s showing a 

charge of five shillings a cake. 

Gamboge 

A native yellow gum from Thailand. A bright transparent 

golden yellow for glazing or water-colour, it is not a 

true pigment. It has been in use since medieval times. 

J Smith in The Art of Painting in Oyl, published in 1701, 

describes a method for preparing the colour, which 

usually comes in rough cylinders about 2.5 in (6 cm) in 

diameter. ‘For a Yellow Gumboge is the best, it is sold 

at Druggist in Lumps, and the way to make it fit for use, 

is to make a little hole with a knife in the lump, and put 

into the hole some water, stir it well with a pencil till 

the water be either a faint or a deeper Yellow, as your 

occasion requires, then pour it into a Gally-Pot, and 

temper up more, till you have enough for your purpose.’ 

(Pencil here would mean a small, soft, hair brush.) 

Geranium Lake 

A fugitive pigment made from Eosine that was in vogue 

during the late 19th century and early 20th century. 

Van Gogh used it in versions of his Sunflowers. Now 

obsolete. 

Giallorino 

A lead yellow pigment likely to have been Naples 

Yellow. The Florentine painter Cennino Cennini 

mentions that Giallorino is associated with volcanoes 

but artificially made. This coincides with Naples yellow, 

which in Antiquity was collected as natural deposits 

from Mount Vesuvius, but by Cennini’s time had been 

synthesised. Another possibility is that the name refers 

to Lead-Tin Yellow (see below). 

Terre Verte, Stone Green, Verdetta, and Celadonite, it 

is a natural green pigment varying in composition and 

shade of colour. It has weak hiding power but is resistant 

to light and chemicals. Highly popular in medieval 

painting for underpainting of flesh tones, it fell from 

favour after the Renaissance. 

Gypsum 

The favourite white pigment of Ancient Egypt, Gypsum 

is a natural mineral Calcium Sulfate which performs well 

in water based mediums but not in oils. 

Han Blue, Han Purple 

Also known as Chinese purple and Chinese blue, 

these synthetic barium copper silicate pigments 

were formulated in China around 250 BCE, and used 

extensively by Chinese artists from the Western Zhou 

period (1207-771 BCE) until the end of the Han dynasty 

(c.220 CE). Pure Han purple - the more popular of the 

two, as Azurite Blue was also in wide use - is actually a 

dark blue, similar to electric indigo. It was first used to 

paint parts of the Terracotta Army Warriors (the huge 

army of clay figurines found near the tomb of Emperor 

Qin Shi Huang). Both pigments were used to colour 

ceramic ware, metalwork, and mural paintings. 

Hooker’s Green 

The earliest forms of this pigment were a mixture of 

Gamboge and Prussian Blue. Later, more lightfast 

variants were created with Aureolin. Modern Hooker’s 

Green is typically a blend of Phthalo Blue and Cadmium 

Yellow. 

Indian Yellow 

This clean, deep and luminescent yellow pigment 

(also called Puree, Peoli, or Gaugoli), was introduced 

to India from Persia during the 15th century. Indian 

Yellow was produced by heating the urine of cattle fed 

on mango leaves, a cruel process ultimately banned 

in 1908. The pigment was popular with both oil and

watercolour painters because of its body and depth of 

tone. Relatively stable, it could be combined with all 

other pigments and its lightfastness in oil paintings was 

enhanced when isolated between layers of varnish. 

Indigo 

A deep blue colour pigment made from the Indigofera 

family of plants until 1870, when it was created 

synthetically. It was used by ancient Egyptian, Greek and 

Roman painters. The pinkish skies to be seen in English 

watercolours of the 18th and early 19th centuries were 

originally greyish-blue, except the Indigo they contained 

has now faded to leave the ochre element of the original 

mixture used by the watercolourist. Natural Indigo was 

superceded in the 19th century by a synthetic colour. 

See Woad (below). 

Lac 

A red colourant originally made in India, which gave 

rise to the term “Lake”, meaning any transparent dyebased 

colour precipitated on an inert pigment base, 

used for glazing. During the High Renaissance in Italy, 

Lac was the third most expensive pigment (after gold 

and Ultramarine), but most artists thought it worth the 

expense. 

Lapis Lazuli (Ultramarine) 

The source of the fabulous, absolutely permanent 

and non-toxic natural blue pigment Ultramarine, the 

precious stone Lapis Lazuli is found in Central Asia, 

notably Afghanistan. It was employed in Ancient times 

as a simple ground up mineral (Lapis Lazuli or Lazuline 

Blue) with weak colour power. Then Persian craftsmen 

discovered a means of extracting the colouring agent, 

creating at a stroke a hugely important art material. 

Ultramarine arrived in Venice on Arab boats, during 

the Renaissance, and was named the pigment from 

overseas (“ultra marine”). Such was its brilliance that 

it rapidly attained a price that only princes and large 

wealthy religious organizations could afford it. Although 

strongly associated with Renaissance art, it is still widely 

used by contemporary painters, especially since prices 

and supply have improved. Synthetic Ultramarine is 

chemically identical, although typically appears in a 


more reddish shade. However its far lower price will 

no doubt ensure that genuine Ultramarine remains in 

limited usage. 

Lead-Tin Yellow 

A highly stable bright opaque yellow was used from 

around 1250 until the mid-17th century, when its use 

ceased abruptly for no obvious reason. Experts believe 

that its formula might have been lost due to the death 

of its producer. Very popular with Renaissance painters, 

who used it in foliage along with earth pigments, 

Lead-Tin Yellow seems to have many of the attributes 

of modern Cadmium Yellow, but little was known about 

it until the 1940s. Since then it has enjoyed a modest 

recovery. 

Lead White 

Also called Flake White, Flemish White, Cremnitz White, 

and Silver White, this is one of the most ancient of 

man-made pigments and the oldest white colourant 

still employed by modern artists. Used since Ancient 

Antiquity, lead white was the only white pigment in 

European easel-painting until the 19th century. Among 

its many attributes, it has the warmest masstone of all 

the white pigments. In addition, it possesses a heavy 

consistency, a very slight reddish-yellow undertone and 

dries faster than any similar colour, making ideal for 

‘alla prima’ techniques. And while its lead carbonate is 

toxic, and therefore not incorporated into water soluble 

paints, its use in oils appears relatively safe. It still 

appears on the palettes of artists today, but has been 

largely superceded by titanium white. 

Lemon Yellow 

An umbrella term for three yellows introduced during 

the 1830s: Strontium Yellow, Barium Yellow and Zinc 

Yellow. All were semi-transparent and used in both oil 

and watercolour paints. Strontium Yellow was a cool, 

light yellow, more permanent and richer in tone than 

Barium Yellow. Rarely used today. 

Logwood 

A blackish colourant derived from a South American 

tree, available in a wide range of colours including blues

Colour Names 

and black, reds and purples. As a painting pigment 

it was used largely as an ink, although the brownish 

and reddish hues would sometimes be employed as 

transparent glazes. 

Madder 

A natural plant colourant obtained from Madder plants 

in a process dating back to Antiquity. 

It was brought back to Europe during the time of 

the Crusades. It was one of the most stable natural 

pigments. Dyes derived from the root of the Madder 

plant were used in ancient Egypt for colouring textiles. 

Later natural madder pigments were used by 15th and 

16th-century painters. After a synthetic version was 

invented in 1868 by the German chemists, Grabe and 

Lieberman, natural production virtually ceased. 

Malachite 

A comparatively permanent pigment of varying colour, 

notably bright green, Malachite (also known as Mineral 

Green or Verdeazzuro) is said to be the oldest known 

green pigment. Traces of it have been discovered in 

Ancient Egyptian tomb paintings as far back as the 

Fourth dynasty. Since Antiquity, it was most popular 

during the European Renaissance period. Eventually 

synthesized, it was marketed under the name Bremen 

Green. Now obsolete. 

Manganese Blue 

A form of Barium Manganate, Manganese Blue has been 

produced since the 19th century. A synthetic variation 

was created in 1935, but both have been superceded by 

more intense blues. Now obsolete. 

Manganese Violet 

Developed by the German chemist E. Leykauf in 1868, 

this pigment - also referred to as Permanent Violet, 

Nuremberg Violet and Mineral Violet - superceded 

Cobalt Violet in 1890. It proved a cleaner alternative 

with less toxicity and improved opacity. 

Massicot 

An obsolete pigment prepared from lead oxide with 

possibly tin oxide. In use from the 14th to the 18th 

century in Europe. Hilliard found it helpful and told that 

it should be used with sugar candy, which could have 

made for problems as massicot is very poisonous. It 

tended to discolour and turn grey with exposure to the 

air. 

Maya Blue 

A highly resiliant bright blue to greenish-blue pigment 

developed by the Maya and Aztec cultures of pre- 

Columbian Mesoamerica. It is a composite of organic 

and inorganic compounds, notably indigo dye from 

the Indigofera suffruticosa plants. Originating at the 

beginning of the 9th century CE, it was in use as late as 

the 16th century in Mexico, in the paintings of the Indian 

Juan Gerson. It survived in Cuba until the 19th century. 

Minium 

The Roman term for Red Lead pigment, a popular 

paint colour used in medieval book illustration and 

calligraphy. A rather dull red prone to darkening, it has 

not been used by modern painters for many decades. 

Mosaic Gold 

An imitation gold pigment (also known as Aurium 

Musicum and Purpurinus), it was used extensively 

by Renaissance painters and book illuminators. Now 

obsolete. 

Mummy 

Also called Egyptian brown, this warm dark-brown 

colourant was obtained from the ground remains of 

Egyptian mummies, a ghoulish practice which was 

eventually banned. Now obsolete. 

Naples Yellow 

Also called Antimony Yellow and Juane Brilliant, Naples 

Yellow is a pale but warm yellow pigment derived from 

Lead Antimoniate. Its use as a painting-pigment can 

be traced back to around 1400 BCE, making it one of 

the oldest synthetic pigments. It possesses very good 

hiding power and good stability. Now obsolete, due to 

its toxicity. See Giallorino (above).

Neutral Grey Tint 

A prepared artist’s colour made up from lampblack, 

Winsor blue and a little alizarin crimson. Popular for 

monochrome work or rendered drawings. 

Ochres (Red/Yellow Ochre) 

The most ancient of all natural colourants, ochre 

is naturally tinted clay containing ferric oxide, and 

produces an earthy pigment varying in colour from 

cream and light yellow to brown or red. Used widely in 

prehistoric rock art, notably in cave murals at Lascaux 

and Chauvet, and also at Blombos cave. Ochres vary 

considerably in transparency - some are opaque, while 

others are used as 

transparent glazes. Can be safely mixed with other 

pigments. 

Orpiment 

A rich lemon or canary yellow with reasonable covering 

power and moderate chemical stability, Orpiment is a 

very ancient natural pigment first used in the Middle 

East and Asia around 3100 BCE. It was imported into 

Venice from Turkey during the Renaissance - yet another 

reason why Venice led the way in artist pigments and 

colourism. It could not be combined with lead or 

copper pigments such as lead white, lead-tin yellow, 

or verdigris, as the mixture is prone to darkening. A 

synthetic version of Orpiment, called Kings Yellow, was 

eventually produced but proved highly toxic due to its 

high level of arsenic. Both were rendered obsolete by 

Cadmium Yellow. 

Payne’s Grey 

Named after the 18th century watercolourist William 

Payne, this very dark blue-grey colourant combines 

ultramarine and black, or Ultramarine and Sienna. It was 

used by artists as a pigment, and also as a mixer instead 

of black. 

Palette 

For details of colour palettes and for details of 

pigments, dyes and colours associated with different 

eras in the history of art, see: Prehistoric Colour Palette 

(Hues used by Stone Age cave painters); 


Egyptian Colour Palette (Hues used in Ancient Egypt); 

Classical Colour Palette (Pigments used by painters 

in Ancient Greece and Rome); Renaissance Colour 

Palette (Colourts used by oil-painters and fresco artists 

in Florence, Rome and Venice); Eighteenth Century 

Colour Palette (Hues used by Rococo and other artists). 

Nineteenth Century Colour Palette (Pigments used by 

Impressionists and other 19th century artists). 

Persian Red 

Also known as Persian Gulf Red, this is a deep reddish 

orange earthy iron pigment from the Persian Gulf, 

made from a silicate of iron and alumina, combined with 

magnesia. It is also known as artificial vermillion. See 

also Venetian Red (below). 

Phthalocyanine Blue 

A very powerful blue lake, produced from copper 

phthalocyanine. In its prime state it is so strong that 

there is no sign of blue, almost black with a coppery 

sheen. Introduced into England in 1935, replacing 

Prussian blue for many artists. Trade names include 

Monastral, Winsor, Thalo and Bocour blue. 

Pink 

The word pink was used for yellow when referring 

to a yellow pigment certainly up to the end of the 

17th century and it is likely well into the 18th. The 

pink (yellow) was made by a skill in cooking. Several 

ingredients were used including: unripe buckthorn 

berries, weld, broom. Norgate in his treatise mentions 

‘callsind eg shels and whitt Roses makes rare pinck that 

never starves’. 

Platina Yellow 

An expensive lemon yellow pigment obtained from 

platinum. Now obsolete, it was replaced by the Chrome 

yellows - Strontium Yellow, Barium Yellow, and Zinc 

Yellow. 

Prussian Blue 

Known also as Berlin Blue, Bronze Blue, Chinese Blue, 

Iron Blue, Milori Blue, Parisian Blue, Paste Blue, and 

Steel Blue, this dark-blue was the first modern, man-

Colour Names 

made pigment. It was developed accidentally by the 

Berlin chemist Diesbach in about 1704, and became 

available to artists’ palettes from 1724, Prussian Blue has 

excellent tinting strength but is only fairly permanent 

to light and air. A popular alternative at the time to 

Indigo dye, Smalt, and Tyrian purple, all of which tend 

to fade, and the extremely costly ultramarine, the first 

famous painters to use it included Pieter van der Werff 

and Antoine Watteau. Outside Europe, the pigment 

was taken up by Japanese painters and woodblock print 

artists. Prussian Blue turns slightly dark purple when 

dispersed in oil paint. 

Quercitron Yellow 

Obsolete yellow obtained from the bark of the black 

quercitron oak from America. It was introduced to 

Europe by Edward Bancroft, a Doctor of Medicine 

and Fellow of the Royal Society, in 1775. It appeared 

in Ackermann’s treatise in 1801 masquerading as: 

‘Ackermann’s Yellow, another new Colour, lately 

discovered, is a beautiful warm rich Yellow, almost the 

tint of Gallstone, works very pleasant, and is very useful 

in Landscapes, Flowers, Shells, etc.’ 

Realgar 

A red-orange pigment chemically related to the yellow 

orpiment, the mineral ore Realgar is an ancient pigment 

used in Egypt, Mesopotamia and Asia Minor until the 

19th century. Now obsolete. 

Red Iron Oxide Artist Pigments 

Ever since Paleolithic artists began painting cave murals, 

Red Iron oxide ore has been a common source for a 

wide variety of artist hues. Locations of its extraction 

are evident in some of the pigment names used, such as 

Venetian Red, Sinopia, Venice Red, Turkey Red, Indian 

Red, Spanish Red, Pompeian Red, and Persian Red. A 

variant of the latter (Persian Gulf Red) is still reputed to 

be the best grade for the natural pigment. Nowadays, 

most Red Iron Oxide colours are manufactured 

synthetically. 

Safflower Pigment 

Commonly known as Carthame, this fugitive red lake 

derives from the flowers of the Safflower plant. Now 

obsolete. 

Saffron 

Another fugitive yellow dye created from the flowers 

of an Indian plant, Saffron pigments were used from 

Antiquity until the 19th century. Still in use by traditional 

craftspeople on the Indian sub-continent and in South- 

East Asia. 

Sandaraca 

A Greco-Roman term used to describe a number of lead 

and arsenic yellows, as well as Cinnabar and even red 

earths. 

Sap Green 

Derived from the unripe berries of the Buckthorn shrub. 

It is highly fugitive, as is a sister-pigment, Iris Green 

which comes from the sap of the Iris Flower. During the 

Middle Ages, Sap Green was reduced to a heavy syrup 

and sold in liquid form. Today’s synthetic Sap Greens 

are lakes obtained from coal tar. 

Saxon Blue 

Alternative name for Smalt (see below). 

Scheele’s Green 

Also known as Schloss Green, this yellowish-green 

pigment was invented in 1775 by Carl Wilhelm Scheele 

and was used by artists in the 18th and 19th centuries. 

It is related to the later Emerald Green. By 1900, these 

greens (both being highly toxic and prone to darkening) 

were made obsolete by zinc oxide and cobalt green, 

also known as zinc green. 

Sepia 

Originally an 18th century replacement for the brown 

pigment Bistre, this natural organic colourant is made 

from the ink sacs of the cuttlefish. Originally used by 

artists in ink painting, illustration and calligraphy, the 

name Sepia is now used in connection with modern oil

paints derived from Burnt Umber, Van Dyke Brown and 

Carbon Black. 

Sienna 

A native clay that contains iron and manganese. In the 

raw state it has the appearance of dark and rich yellow 

ochre. Burnt sienna is made by calcining or roasting the 

raw sienna in a furnace. The two, raw and burnt siennas 

are amongst the most stable pigments on the painter’s 

palette. 

Sinopia 

An ancient name for native red iron oxides, it takes its 

name from the town of Sinope in Asia Minor. Cennini 

says in Il Libro dell’ Arte of its unsuitability for fresco and 

tempera. Well watered down it was much employed by 

artists for laying in the under-drawing for fresco work on 

the arriccio. 

Smalt 

Made from ground blue-coloured glass, Smalt was 

the earliest of the cobalt pigments. It emerged as a 

European replacement for Egyptian Blue, which was 

derived from copper. Despite its weak tinting power it 

remained popular until the development of synthetic 

Ultramarine and Cobalt Blue in the 19th century. 

Production continued intermittently until 1950. 

Terra Marita 

A fugitive Yellow lake derived from the Saffron plant. 

Titanium White 

The strongest, most brilliant, most stable white pigment 

available to painters in the history of art. Although 

discovered in 1821, mass-production of the artist-quality 

oil pigment only began in the early 1920s. Its masstone, 

neither warm nor cool, lies mid-way between lead white 

and zinc white. It is now the world’s primary pigment 

for whiteness, brightness and opacity. Available for oils, 

watercolours and acrylic painting. 

Turner’s Yellow 

Named for the inventor, not the English watercolourist 

artist, this lead pigment was popular for a spell due 


to its cheap cost, although it was prone to both 

impermanence and blackening. Hues ranged from 

bright yellow to orange. Now obsolete. 

Turnsole 

A natural purplish pigment (also called Heliotropum) 

obtained from the Mediterranean Heliotrope plant 

of the borage family. Used as an artists colourant, it 

makes a number of lakes in blue and red colours. Was 

sometimes mistaken for the similarly coloured Woad. 

Both Turnsole and Woad were employed in book 

illumination under the umbrella term Folium (see above). 

Turpeth Mineral 

Mercuric sulphate. Highly poisonous. Valued at one 

time for the fine greens it produced when mixed with 

Prussian blue. Discarded because it decomposed and 

turned black in some mixtures. 

Tyrian Purple 

A colour derived from shell fish by the Phoenicians and 

made famous as the colour worn by Roman Caesars. 

Also used by artists in Antiquity as a glazing pigment. 

Available in shades of violet, true purple, and an 

exceptionally deep crimson, its use was limited by its 

huge cost of production. 

Ultramarine 

Natural Ultramarine, made from the precious stone 

Lapis Lazuli, was (and remains) one of the world’s 

most expensive artists’ pigments. A cool deep blue 

hue, it was first used in 6th century Afghanistan, and 

the pigment achieved its zenith during the Italian 

Renaissance as it harmonized perfectly with the 

vermilion and gold of illuminated manuscripts and 

Italian panel painting. However, being vulnerable to 

even minute traces of mineral acids and acid vapours, it 

was only used for frescoes when it was applied “secco” 

(when the pigment was mixed with a binding medium 

and applied over dry plaster) as in Giotto di Bondone’s 

famous fresco cycle in the Cappella degli Scrovegni 

Chapel in Padua. Ultramarine was finally synthesized 

independently by both the Frenchman Jean Baptiste 

Guimet and the German chemist Christian Gottlob

Colour Names 

Gmelin in the late 1820s/ early-1830s. The artificial 

colourant was non-toxic and as permanent as the natural 

variety but darker and less azure. It was formulated for 

both oil and watercolour paints. 

Ultramarine Ashes 

The secondary product remaining after the prime 

quality Ultramarine Blue has been removed from the 

Lapis Lazuli stone. Containing only traces of the genuine 

Ultramarine, it is a permanent but weak grey-blue 

colour. 

Ultramarine Green 

This variant of synthetic Blue Ultramarine is a weak 

bluish green of low tinting strength. In vogue between 

about 1850 and 1960, it is rarely seen today. 

Uranium Yellow 

A bright light yellow pigment with green efflorescence, 

its production as a colourant was banned due to its 

perceived radioactivity. In fact it emitted no more than 

the human body. 

Umber 

Used as a paint-colourant since prehistoric times, Umber 

is a natural brown clay pigment containing iron and 

manganese oxides. Heating intensifies the colour, and 

the resulting pigment is commonly called burnt umber. 

It was originally mined in Umbria, a region of central 

Italy, although the finest quality umber comes from 

Cyprus. 

Van Dyke Brown 

Known also as Cassel Earth, Rubins Brown, and Cologne 

Brown, this transparent brown pigment dates from the 

17th century and is a mixture of clay, iron oxide, humus 

and bitumen. Its transparency made it superior to 

umbers and ochres for glazing, although it was prone 

to fading and, because of its bitumen (Asphaltum) 

component, to cracking. 

Venetian Red 

The term Venetian Red usually refers to a specific bluish 

tone of Red Oxide, although some variations can be 

orange-ish or violet in hue. See also Persian Red (above). 

Verdaccio 

A neutral greenish colour usually obtained from mixing 

together left over paint on the palette, it was commonly 

used during Renaissance times for working up a drawing 

to the painting stage, or for underpainting flesh tones in 

Medieval art. 

Verdigris 

A common synthetic green pigment used from Classical 

Antiquity until the 19th century, it was the most vibrant 

green available during the Renaissance and Baroque 

eras. Its relative transparency led to it being frequently 

combined with lead white or lead-tin yellow, or used 

as a glaze. The name derives from the Old French 

word “vertegrez”, meaning “green of Greece”. Its use 

declined sharply from the 18th century onwards. 

Vermilion (Vermillion) 

An orange-ish red pigment with fine hiding power 

and good permanence, but high toxicity. Natural 

Vermilion, known to the Romans as Minium, comes 

from the mineral ore Cinnabar (see above), and the 

name Vermilion is most commonly used to describe the 

synthetic version of the pigment, which nowadays is 

usually obtained by reacting mercury with molten sulfur. 

In Antiquity, Vermilion/Cinnabar was highly prized, 

being ten times more expensive than red ochre. Later it 

was an important colourant in illuminated manuscripts, 

although it remained prohibitively expensive until 

the 14th century when a synthetic version was first 

produced. Vermilion was the traditional red pigment in 

Chinese art, and is the colourant used in Chinese red 

lacquer. Today, Vermilion has been replaced in painting 

by the pigment cadmium red. 

Viridian Green 

Discovered in 1797 by the French chemist Vauquelin, 

it wasn’t fully developed into an artist paint hue until 

about 1840. A very stable, powerful cold green it 

possessed excellent permanence and lack of toxicity,

and superceded a fugitive colour known as Emerald 

Green, whose name it took until it became widely known 

as Viridian. 

Weld 

A common plant-based Yellow Lake, it was one of the 

most popular organic yellows before the introduction 

of the modern synthetics. Quercitron and Buckthorn 

berries were better known but no more common among 

painters than Weld. More suitable than its rivals for 

creating opaque yellows, it was used as an alternative to 

Orpiment. 

White Lead 

See Lead White (above). 

Woad 

An ancient pigment obtained from the woad or dyers 

woad herb of the mustard family, grown for its blue/ 

indigo dye and pigment. Derived from the Saxon word 

“waad”, it is the weaker European equivalent of the 

more famous colourant made from the Indigofera plant, 

with which it was sometimes mixed. 

Zinc White 

Zinc oxide was recognized as a possible source of artistwhite 

by the French in the 1780s. After the discovery of 

zinc deposits in Europe during the 18th century, patents 

were granted for the manufacture of zinc oxide to the 

English colourmaker John Atkinson, and others. By the 

early 1830s, Zinc White was accepted as a watercolour 

although it took longer to formulate it for use in artist oil 

paints. In 1834, Winsor and Newton, Limited, of London, 

presented a dense form of zinc oxide which was sold as 

Chinese white. The name stemmed from the popular 

oriental porcelain in circulation in the 19th century. But 

the chemist George H. Backhoffner of London who 

lectured widely in the Art Academies recommended 

Flemish white (Lead White) as superior so in 1837, 

Winsor and Newton published a convincing response 

to Backhoffner. In 1844, a superior Zinc White for oils 

was produced by LeClaire in Paris. In comparison with 

Lead White, Zinc White is a slower drying pigment, less 


opaque, more permanent and less prone to blackening. 

It is also non-toxic and more economical. Tints made 

with Zinc White show greater nuances than tints made 

with other whites. Also, Zinc White has a much colder, 

cleaner, whiter masstone than the best grades of lead 

white or even titanium white. Its drawback is that it 

makes a rather brittle dry paint film when used unmixed 

with other colours, which can cause cracks in paintings 

relatively quickly. 

Zinc Yellow 

A pale greenish semi opaque pigment more suitable for 

oil paint than watercolours, this synthetic Zinc Chromate 

was available for some 150 years (c.1850-1990) and 

possessed excellent lightfastness. But its chrome 

content made it quite toxic. 

Reference: Encyclopedia of Irish and World Art, 2010. Artists Colour Pigments. [online] Available at: 


Colour Names 

Yves Klein’s Artwork 

Monochrome works: The Blue Epoch 

Although Klein had painted monochromes as early as 

1949, and held the first private exhibition of this work 

in 1950, his first public showing was the publication of 

the Artist’s book Yves: Peintures in November 1954. 

Parodying a traditional catalogue, the book featured 

a series of intense monochromes linked to various 

cities he had lived in during the previous years. Yves: 

Peintures anticipated his first two shows of oil paintings, 

at the Club des Solitaires, Paris, October 1955 and Yves: 

Proposition monochromes at Gallery Colette Allendy, 

February 1956. Public responses to these shows, 

which displayed orange, yellow, red, pink and blue 

monochromes, deeply disappointed Klein, as people 

went from painting to painting, linking them together as 

a sort of mosaic. 

From the reactions of the audience, [Klein] realized 

that...viewers thought his various, uniformly colored 

canvases amounted to a new kind of bright, abstract 

interior decoration. Shocked at this misunderstanding, 

Klein knew a further and decisive step in the direction 

of monochrome art would have to be taken...From that 

time onwards he would concentrate on one single, 

primary color alone: blue. 

The next exhibition, ‘Proposte Monochrome, Epoca Blu’ 

(Proposition Monochrome; Blue Epoch) at the Gallery 

Apollinaire, Milan, (January 1957), featured 11 identical 

blue canvases, using ultramarine pigment suspended in 

a synthetic resin ‘Rhodopas’. Discovered with the help 

of Edouard Adam, a Parisian paint dealer, the optical 

effect retained the brilliance of the pigment which, when 

suspended in linseed oil, tended to become dull. Klein 

later patented this recipe to maintain the “authenticity 

of the pure idea.” This colour, reminiscent of the lapis 

lazuli used to paint the Madonna’s robes in medieval 

paintings, was to become famous as ‘International Klein 

Blue’ (IKB). The paintings were attached to poles placed 

20 cm away from the walls to increase their spatial 

ambiguities. 

Reference: Wikipedia, 2011. Yves Klein. [online] Available at: [Accessed 13/04/11]. 

The show was a critical and commercial success, 

traveling to Paris, Düsseldorf and London. The Parisian 

exhibition, at the Iris Clert Gallery in May 1957, became 

a seminal happening. To mark the opening, 1001 

blue balloons were released and blue postcards were 

sent out using IKB stamps that Klein had bribed the 

postal service to accept as legitimate. Concurrently, an 

exhibition of tubs of blue pigment and fire paintings was 

held at Gallery Collette Allendy. 

For his next exhibition at the Iris Clert Gallery (April 

1958), Klein chose to show nothing whatsoever, called 

La spécialisation de la sensibilité à l’état matière 

première en sensibilité picturale stabilisée, Le Vide 

(The Specialization of Sensibility in the Raw Material 

State into Stabilized Pictorial Sensibility, The Void): he 

removed everything in the gallery space except a large 

cabinet, painted every surface white, and then staged 

an elaborate entrance procedure for the opening night; 

The gallery’s window was painted blue, and a blue 

curtain was hung in the entrance lobby, accompanied 

by republican guards and blue cocktails. Thanks to an 

enormous publicity drive, 3000 people were forced to 

queue up, waiting to be let in to an empty room. 

Recently my work with color has led me, in spite of 

myself, to search little by little, with some assistance 

(from the observer, from the translator), for the 

realization of matter, and I have decided to end the 

battle. My paintings are now invisible and I would like 

to show them in a clear and positive manner, in my next 

Parisian exhibition at Iris Clert’s. 

Later in the year, he was invited to decorate the 

Gelsenkirchen Opera House, Germany, with a series of 

vast blue murals, the largest of which were 20 metres 

by 7 metres. The Opera House was inaugurated in 

December 1959. Klein celebrated the commission by 

travelling to Cascia, Italy, to place an ex-voto offering 

at the Saint Rita Monastery. The offering took the form 

of a small transparent plastic box containing three 

compartments; one filled with IKB pigment, one filled 

with pink pigment, and one with gold leaf inside. The 

container was only rediscovered in 1980.

International Klein Blue 

International Klein Blue (IKB) is a deep blue hue first 

mixed by the French artist Yves Klein. IKB’s visual impact 

comes from its heavy reliance on Ultramarine, as well as 

Klein’s often thick and textured application of paint to 

canvas. 

History 

International Klein Blue (or IKB as it is known in art 

circles) was developed by French artist Yves Klein as 

part of his search for colors which best represented 

the concepts he wished to convey as an artist. IKB was 

developed by Klein and chemists to have the same 

color brightness and intensity as dry pigments, which 

it achieves by suspending dry pigment in polyvinyl 

acetate, a synthetic resin marketed in France as 

Rhodopas M or M60A by the firm Rhône Poulenc. 

While it is often said that the method for creating 

International Klein Blue was patented by the artist, this is 

not entirely true. Klein’s patent had little to do with the 

chemical composition of the color, instead describing 

a method by which Klein was able to distance himself 

from the physical creation of his paintings by remotely 

directing models covered in the color. 

Usage in Yves Klein’s art 

Although Klein had worked with blue extensively in 

his earlier career, it was not until 1958 that he used 

it as the central component of a piece (the color 

effectively becoming the art). Klein embarked on a 

series of monochromatic works using IKB as the central 

theme. These included performance art where Klein 

painted models’ naked bodies and had them walk, 

roll and sprawl upon blank canvases as well as more 

conventional single-color canvases. 


Reference: Wikipedia, 2011. International Klein Blue. [online] Available at: [Accessed 13/04/11].

Colour Names 

Top 10 Weird Colors You’ve Never Heard Of by Ash Grant 

Colors. We’ve seen them. We’ve had to recite them. 

We know them. We use them each and every day. 

You probably know your basic colors such as red, 

green, blue, yellow, orange, pink, purple, and possibly 

many more. You may know that the primary colors 

are red, blue, and yellow and that they can’t be made 

through the mixing of other colors. You may also know 

secondary colors, those created by mixing two primary 

colors, such as purple, green, and orange. 

But, there are surely some colors that you’ve never 

heard of or maybe even seen, thanks to Crayola and the 

growing popularity of online use of RGB codes as well 

as Hex codes for layouts and design. Below is a list of 

colors you may not know exist. You’ve maybe seen them 

a few times before, but it’s safe to say you don’t know 

their names. 

10. Malachite 

Malachite is probably a color we’ve all seen, but never 

known by its “real” name. This color is also known as 

basic green 4 and is often used when creating a green 

dye. This vibrant green comes from the carbonate 

mineral known as Malachite, or copper carbonate. In 

the 1800, the mineral was widely used for green paints 

because it was lightfast and often varied in color. The 

color is one that is seen rampant in history. For instance, 

there is the Malachite Room in Hermitage, and it is also 

said that Demeter’s throne was made of this color as 

well. 

9. Gamboge 

Think of spicy mustard and think of gamboge, but a 

little bit darker. The color is a yellow pigment that is 

somewhat transparent, despite its dark tint. The color 

is named after the gamboge tree, which is known for its 

yellow resin. The color comes from Cambodia, where 

in the 12th century painters would use the color as a 

watercolor paint. Besides being used as a watercolor, 

the color has also been used as a varnish for wood. 

Gamboge as a color started to spread, and in the 17th 

century made its way to Europe, where it was first used 

in the English language in 1634. 

8. Fallow 

Sounds like a word you’d hear out of someone with a 

heavy Southern accent. Maybe something like “Fallow 

me right over yonder.” Fortunately, fallow is a word. In 

fact, it is one of the oldest color names to ever exist 

in the English language. Though not a considered a 

“pretty” color by some, the pale brown is named after 

the color many would see when looking into fallow fields 

as well as the soil, which was often sandy. The word 

fallow, to express the color, was first recorded in 1000. It 

is said that the color is also known in South African and 

Indian cultures as Ravi brown. 

7. Razzmatazz 

Not the liquor; nor the song, nor the television series, 

razzmatazz is red-pink color that was invented by 

Crayola in 1993, and was first found in the Big Box of 96. 

The color is said to be one very similar to rose, which 

is found directly in the middle of magenta and red on 

the color wheel. You can thank Laura Bartolomei-Hill for 

the name, as she was the one who named the color at 

the age of five during Crayola’s Name the New Colors 

Contest. 

5. Falu Red 

Falu red has deep meaning in many different areas 

of Sweden. This color is a dark red tint that was a 

prominent color used on wooden barns and cottages. 

The purpose of the deep red color was to mimic 

the color of more expensive brick homes. The color 

originally came from a copper mine at Falun, which is 

located in Dalarna, Sweden. Unlike most colors, this one

has been around for a long time, since the 16th century 

to be exact, and today is still used. Many realized that 

the color is great to use in order to preserve wood. 

However, it is rarely used for homes in the cities of 

Sweden today, as brick became more popular and many 

wanted a more neutral/lighter colored home. But, in the 

countryside, the color can be seen everywhere. 

5. Arsenic 

It doesn’t take a brain scientist to figure out this color, 

but it’s definitely not a “happy” color, so to speak. 

Imagine saying you want to paint your walls in arsenic, 

semi-gloss. You’d get some looks there. The color 

arsenic is based around the element arsenic which is 

a dark gray-blue color. Arsenic is a metalloid that is 

often naturally found. However, there are other types of 

arsenic that aren’t the gray-blue color. Some are more of 

a red-orange tint. 

4. Feldgrau 

A German color, translating to “field gray,” feldgrau was 

the color of German uniforms worn from 1907 until late 

1945. The color was also used in post war uniforms by 

the East German Army (NVA) and the Bundeswehr, West 

Germany’s army. The color was last used on the woollen 

m/58 winter uniform. The gray-green color is very similar 

to the greens, grays, and browns used in more widely 

used army uniforms, such as those of the U.S. Army. 

3. United Nations Blue 

That’s right, the United Nations, the international 

organization provided to help countries with human 

rights, social progress, economic development and 

more has it’s own color. Originally named United 

Nations blue, the color is very similar to Dodger blue, 

but is more pastel like and not as vibrant. You will find 

this blue on the U.N. flag, as well as their emblem and 

even the U.N. peacekeeper uniforms. 

2. Xanadu 


No this color has nothing to do with Robert Greenwald’s 

film. Instead, Xanadu is said to be a color coming from 

the color of a plant. Xanadu is a green-gray color that 

comes from a plant known as the Philodendron. The 

plant leaves are generally a green color with a tint of 

gray. This plant is widely seen in Australia, but it is said 

that the plant got its name from Xanadu, which was an 

ancient city located in Inner Mongolia, China. 

1. Capuut Mortuum 

If you’re one of those super cool Latin scholars, or 

maybe one who knows a little about alchemy, you may 

have heard the term caput mortuum. In Latin, the words 

translate into “worthless remains” or “dead head.” 

The color name comes from the variety of purples and 

brownish colors that are created when iron oxide, A.K.A. 

rust is oxidized. It is said that the color was widely used 

when painters would paint important people or religious 

figures such as patrons. It’s a highly popular color used 

in dying paper as well as oil paints. 

Reference: GRANT, A., 2009. Top 10 Weird Colors You’ve Never Heard Of, [online] Available at: [Accessed 12/04/11].

Colour Names 

32+ Common Color Names for Easy Reference – Colourlovers blog post 

Are you tired of the other kids at Summer Color Camp 

laughing at you because you thought chartreuse was a 

shade of pink? Still having nightmares about the time 

you called a Salmon colored dress Mauve? With our help 

you’ll never call Azure, Aquamarine again and will be 

name dropping colors like they’re hot potatoes. 

Our guide isn’t an exact science, depending on our 

monitor color calibration, brightness and even how 

good our eyes are... we all see a slightly different color. 

So, we took the color names that are used most often 

and best guessed the appropriate colors based on web 

standards and common usage. 

Ivory 

HEX: #FFFFF0 

RGB: 255, 255, 240 

CMYK: 1, 0, 6, 0 

Beige 

HEX: #F5F5DC 

RGB: 245, 245, 220 

CMYK: 1, 0, 24, 0 

Wheat 

HEX: #F5DEB3 

RGB: 245, 222, 179 

CMYK: 4, 11, 32, 0 

Tan 

HEX: #D2B48C 

RGB: 210, 180, 140 

CMYK: 18, 28, 48, 0 

Khaki 

HEX: #C3B091 

RGB: 195, 176, 145 

CMYK: 25, 28, 45, 0 

Silver 

HEX: #C0C0C0 

RGB: 192, 192, 192 

CMYK: 25, 20, 20, 0 

Gray 

HEX: #808080 

RGB: 128, 128, 128 

CMYK: 52, 43, 43, 8 

Charcoal 

HEX: #464646 

RGB: 70, 70, 70 

CMYK: 67, 60, 58, 42

Navy Blue 

HEX: #000080 

RGB: 0, 0, 128 

CMYK: 100, 98, 14, 17 

Royal Blue 

HEX: #084C9E 

RGB: 8, 76, 158 

CMYK: 100, 80, 4, 0 

Medium Blue 

HEX: #0000CD 

RGB: 0, 0, 205 

CMYK: 52, 43, 43, 8 

Azure 

HEX: #007FFF 

RGB: 0, 127, 255 

CMYK: 77, 51, 0, 0 

Cyan 

HEX: #00FFFF 

RGB: 0, 255, 255 

CMYK: 52, 0, 13, 0 

Aquamarine 

HEX: #7FFFD4 

RGB: 127, 255, 212 

CMYK: 40, 0, 30, 0 

Teal 

HEX: #008080 

RGB: 0, 128, 128 

CMYK: 86, 31, 49, 8 

Forest Green 

HEX: #228B22 

RGB: 34, 139, 34 

CMYK: 47, 0, 100, 0 

Olive 

HEX: #808000 

RGB: 128, 128, 0 

CMYK: 30, 0, 100, 50 

Chartreuse 

HEX: #7FFF00 

RGB: 127, 255, 0 

CMYK: 47, 0, 100, 0 

Lime 

HEX: #BFFF00 

RGB: 191, 255, 0 

CMYK: 30, 0, 100, 0 

Golden 

HEX: #FFD700 

RGB: 255, 215, 0 

CMYK: 1, 13, 100, 0 

Goldenrod 

HEX: #DAA520 

RGB: 218, 165, 32 

CMYK: 15, 35, 100, 1 

Coral 

HEX: #FF7F50 

RGB: 255, 127, 80 

CMYK: 0, 63, 72, 0 

Salmon 

HEX: #FA8072 

RGB: 250, 128, 114 

CMYK: 0, 63, 49, 0 

Hot Pink 

HEX: #FC0FC0 

RGB: 252, 15, 192 

CMYK: 10, 87, 0, 0 


Fuchsia 

HEX: #FF77FF 

RGB: 255, 119, 255 

CMYK: 16, 57, 0, 0 

Puce 

HEX: #CC8899 

RGB: 204, 136, 153 

CMYK: 19, 54, 25, 0 

Mauve 

HEX: #E0B0FF 

RGB: 224, 176, 255 

CMYK: 27, 41, 0, 0 

Lavender 

HEX: #B57EDC 

RGB: 181, 126, 220 

CMYK: 35, 55, 0, 0 

Plum 

HEX: #843179 

RGB: 132, 49, 121 

CMYK: 55, 95, 20, 4 

Indigo 

HEX: #4B0082 

RGB: 75, 0, 130 

CMYK: 86, 100, 11, 8 

Maroon 

HEX: #800000 

RGB: 128, 0, 0 

CMYK: 29, 100, 100, 38 

Crimson 

HEX: #DC143C 

RGB: 220, 20, 60 

CMYK: 7, 100, 78, 1 

Reference: RUCIEN, 2007. 32+ Common Color Names for Easy Reference. Colourlovers, [blog] 24 July, Available at: 


Colour Names 

List of Colours – From my Own Knowledge 


Almond 

Amber 

Amethyst 

Antique Gold 

Apple Green 

Aqua 

Aquamarine 

Aubergine 

Auburn 

Azure 

Baby Pink 

Barley 

Beech 

Beige 

Bice 

Biscuit 

Bisque 

Black 

Blond 

Blue 

Brass 

Brick 

Bronze 

Brown 

Buff 

Burgundy 

Burnt Sienna 

Burnt Umber 

Buttermilk 

Cadmium 


Camel 

Caramel 

Carnelian 

Cerise 

Cerulean 

Champagne 

Charcoal 

Chartreuse 

Cherry 

Chestnut 

Chocolate 

Chrome 

Cinnamon 

Coal 

Cocoa 

Cobalt Blue 

Copper 

Coral 


Cream 

Crimson 

Cyan 

Damson 

Dark Blue 

Dark Green 

Drab 

Duck Egg Blue 

Eggshell 

Emerald 

Fawn 

Flesh 

Fuchsia 

Geranium 

Gold 

Granite 

Grass Green 

Green 

Greige 

Grey 

Gunmetal 

Hazel 

Heather 

Honey 

Honeydew 

Hot Pink 

Indigo 

Ivory 

Jade 

Jet Black 

Khaki 

Lavender 

Lemon 

Light Blue 

Light Green 

Lilac 

Lime 

Linen 

Magenta 

Magnolia 

Mahogany 

Marigold 

Maroon 

Mauve 

Melon 

Mint 

Mocha 

Mustard 

Navy Blue 

Nude 

Oatmeal 

Olive 

Opal 

Orange 

Oyster 

Peach 

Peacock Blue 

Pearl 

Periwinkle 

Petrol Blue 

Pewter 

Pine 

Pink 

Pistachio 

Platinum 

Plum 

Porphyry 

Powder Blue 

Puce 

Purple 

Raspberry 

Raw Umber 

Red 

Rose 

Royal Blue 

Royal Purple 

Ruby 

Rust 

Russet 

Sage 

Salmon 

Sand 

Sapphire 

Scarlet 

Sepia 

Shell 

Sienna 

Silver 

Sky Blue 

Slate 

Smoke 

Steel 

Stone 

Tan 

Tangerine 

Taupe 

Tawny 

Teal 

Thistle 


Toffee 

Topaz 

Turquoise 

Ultramarine 

Vermilion 

Violet 

Wheat 

White 

Wine 

Yellow 

Yellow Ochre 

Zinc

From Cabinet Magazine Colour Columns From Never Odd or Even 

Amber 

Ash 

Beige 

Bice 

Black 

Brown 

Chartreuse 

Cyan 

Gold 

Gray 

Green 

Hazel 

Indigo 

Ivory 

Khaki 

Magenta 

Maroon 

Mauve 

Olive 

Opal 

Pink 

Pistachio 

Porphyry 

Prussian Blue 

Puce 

Purple 

Red 

Ruby 

Rust 

Safety Orange 

Scarlet 

Sepia 

Silver 

Sulphur 

Tawny 

Ultramarine 

Verdigris 

Violet 

White 

Yellow 

Amber 

Azure 

Black 

Bronze 

Brown 

Burgundy 

Cherry 

Copper 

Coral 

Cream 

Emerald 

Gold 

Green 

Grey 

Hazel 

Indigo 

Ivory 

Khaki 

Lavender 

Lemon 

Lilac 

Lime 

Mahogany 

Mauve 

Mustard 

Navy 

Ochre 

Olive 

Orange 

Peach 

Pink 

Poppy 

Purple 

Red 

Ruby 

Rust 

Saffron 

Sapphire 

Scarlet 

Silver 


Slate 

Terracotta 

Turquoise 

Violet 

White 

Yellow

Colour Names 

From The Colour Thesaurus by Nathan Moroney 

Acid Green 

Adobe 

Algae 

Amber 

Amethyst 

Apple Green 

Apple Red 

Apple 

Apricot 

Aqua Blue 

Aqua Green 

Aqua 

Aquamarine 

Army Green 

Ash 

Asparagus 

Aubergine 

Auburn 

Autumn Orange 

Avocado 

Azure 

Baby Blue 

Baby Green 

Baby Pink 

Bahama Blue 

Banana 

Barn Red 

Battleship Grey 

Beige 

Berry 

Bisque 

Bittersweet 

Black 

Blood Orange 

Blood Red 

Blue Black 

Blue Grey 

Blue Green 

Blue Purple 

Blue Red 

Blue Sky 

Blue Slate 

Blue Violet 

Blue 

Bluebell 

Blueberry 

Bluish Grey 

Bluish Green 

Bluish Purple 

Blush 

Bottle Green 

Brick Brown 

Brick Red 

Brick 

Bright Aqua 

Bright Baby Blue 

Bright Blue 

Bright Fuchsia 

Bright Grass Green 

Bright Green 

Bright Lavender 

Bright Light Blue 

Bright Light Green 

Bright Lilac 

Bright Lime Green 

Bright Lime 

Bright Magenta 

Bright Orange 

Bright Pink 

Bright Purple 

Bright Red 

Bright Seafoam 

Bright Sky Blue 

Bright Spring Green 

Bright Teal 

Bright Turquoise 

Bright Violet 

Bright Yellow 

Brilliant Blue 

British Racing Green 

Bronze 

Brown Black 

Brown Green 

Brown Red 

Brown 

Brownish Black 

Brownish Green 

Brownish Orange 

Bruised Purple 

Bubblegum 

Buff 

Burgundy 

Burnt Amber 

Burnt Orange 

Burnt Red 

Burnt Sienna 

Burnt Umber 

Burnt Yellow 

Butter 

Buttercup 

Buttermilk 

Butterscotch 

Cadet Blue 

Camel 

Camo Green 

Canary 

Candy Apple Red 

Candy Pink 

Cantaloupe 

Caramel 

Cardinal 

Caribbean Blue 

Carmine 

Carnation 

Carolina Blue 

Cayenne Green 

Celadon 

Celery 

Cerise 

Cerulean 

Charcoal 

Chartreuse 

Cherry 

Chestnut 

Chocolate 

Christmas Green 

Cinnamon 

Citron 

Claret 

Clay 

Cloudy Blue 

Coal 

Cobalt 

Cocoa 

Coffee 

Colonial Blue 

Cool Light Green 

Copper 

Coral 

Cornflower 

Cotton Candy 

Country Blue 

Cranberry 

Cream 

Crimson 

Cyan 

Daffodil 

Dark Aqua 

Dark Beige 

Dark Blue Green 

Dark Blue 

Dark Bluish Green 

Dark Brick 

Dark Brown 

Dark Chocolate 

Dark Cyan 

Dark Forest 

Dark Fuchsia 

Dark Gold 

Dark Grass Green 

Dark Grey 

Dark Green 

Dark Khaki 

Dark Lavender 

Dark Lilac 

Dark Lime 

Dark Magenta 

Dark Maroon 

Dark Mauve 

Dark Navy 

Dark Olive Green

Dark Olive 

Dark Orange 

Dark Peach 

Dark Periwinkle 

Dark Pink 

Dark Purple 

Dark Red 

Dark Rose 

Dark Sage 

Dark Sand 

Dark Sea Green 

Dark Seafoam 

Dark Sky Blue 

Dark Tan 

Dark Teal 

Dark Turquoise 

Dark Violet 

Dark Yellow Green 

Dark Yellow 

Deep Blue 

Deep Burgundy 

Deep Forest 

Deep Green 

Deep Lavender 

Deep Lilac 

Deep Magenta 

Deep Maroon 

Deep Navy Blue 

Deep Ocean Blue 

Deep Pink 

Deep Purple 

Deep Red 

Deep Rose 

Deep Salmon 

Deep Sea Blue 

Deep Sky Blue 

Deep Violet 

Deep Yellow 

Delft Blue 

Denim 

Dijon 

Dirty Brown 

Dirty Green 

Dirty Orange 

Dirty Yellow 

Drab Green 

Drab Olive 

Duck Egg Blue 

Dull Blue 

Dull Green 

Dull Magenta 

Dull Orange 

Dull Purple 

Dull Red 

Dusk 

Dusky Blue 

Dusky Pink 

Dusky Rose 

Dusty Blue 

Dusty Green 

Dusty Pink 

Dusty Purple 

Dusty Rose 

Dutch Blue 

Earth 

Easter Egg Blue 

Easter Green 

Ebony 

Ecru 

Eggplant 

Eggshell 

Electric Blue 

Electric Green 

Electric Lime 

Electric Purple 

Emerald 

Evening Sky Blue 

Evergreen 

Faded Blue 

Faded Green 

Fawn 

Fern Green 

Fire Engine Red 

Firebrick 

Flamingo Pink 

Flat Black 

Fluorescent Blue 

Fluorescent Green 

Fluorescent Lime Green 

Fluorescent Pink 

Foam Green 

Forest 

French Blue 

Fresh Green 

Frog Green 

Fruit Punch 

Fuchsia Pink 

Fuchsia 

Garnet 

Gold 

Golden Brown 

Golden Orange 

Golden Yellow 

Goldenrod 

Golf Green 

Granny Smith Apple 

Grape Jelly 

Grape 

Grapefruit 

Grass Green 

Grey Blue 

Grey Green 

Grey Pink 

Grey 

Greyish Blue 

Greyish Green 

Green Apple 

Green Blue 

Green Brown 

Green Grey 

Green Yellow 

Green 

Greenish Blue 

Greenish Brown 

Greenish Grey 

Greenish Yellow 

Harvest Gold 

Hazel 

Heather 


Highlighter Green 

Hospital Green 

Hot Green 

Hot Lime 

Hot Pink 

Hot Purple 

Hot Red 

Hunter Green 

Ice Blue 

Ice 

Indigo 

Ink 

Ivory 

Jade 

Jet Black 

Jungle Green 

Kelly Green 

Key Lime 

Khaki 

Kiwi 

Lake Blue 

Lapis Lazuli 

Lavender Pink 

Lavender 

Leaf Green 

Lemon Green 

Lemon Lime 

Lemon Yellow 

Lemon 

Lemongrass 

Lichen Green 

Light Aqua 

Light Blue Grey 

Light Blue Green 

Light Blue 

Light Bright Green 

Light Brown 

Light Burgundy 

Light Crimson 

Light Cyan 

Light Forest 

Light Grape 

Light Grey

Colour Names 

From The Colour Thesaurus by Nathan Moroney 

Light Green 

Light Lavender 

Light Lime Green 

Light Lime 

Light Magenta 

Light Maroon 

Light Mauve 

Light Navy Blue 

Light Navy 

Light Neon Green 

Light Olive Green 

Light Olive 

Light Orange 

Light Pink 

Light Purple 

Light Red 

Light Royal Blue 

Light Sea Green 

Light Seafoam 

Light Sky Blue 

Light Teal 

Light Turquoise 

Light Violet 

Light Yellow 

Lilac 

Lime Green 

Lime Ice 

Lime 

Limeade 

Lipstick Red 

Loden 

Luminous Green 

Magenta 

Mahogany 

Maize 

Mandarin 

Mango 

Mardi Gras Purple 

Marigold 

Marine Blue 

Marine 

Maroon 

Mauve 

Meadow 

Mediterranean Blue 

Medium Aqua 

Medium Blue 

Medium Brown 

Medium Grey Blue 

Medium Grey 

Medium Green 

Medium Lavender 

Medium Purple 

Medium Sky Blue 

Medium Yellow 

Melon Green 

Melon 

Merlot 

Midnight Black 

Midnight Blue 

Midnight Purple 

Midnight 

Military Green 

Milk Chocolate 

Mint Green 

Mocha 

Moss Green 

Mud 

Muddy Brown 

Mulberry 

Murky Green 

Mustard Green 

Mustard Yellow 

Mustard 

Muted Fuchsia 

Navy Blue 

Navy Green 

Navy 

Neon Blue 

Neon Green 

Neon Lime 

Neon Pink 

Neon Purple 

Neon Yellow 

New Grass 

Night 

Noir 

Ocean Blue 

Ocean Green 

Ocean 

Ochre 

Off Green 

Off White 

Old Gold 

Old Pin 

Olive Brown 

Olive Drab 

Olive 

Orange Brown 

Orange Red 

Orange Yellow 

Orange 

Orangey Brown 

Orangey Red 

Orangish Red 

Orchid 

Pacific Blue 

Pale Blue 

Pale Fuchsia 

Pale Grey 

Pale Green 

Pale Lavender 

Pale Olive 

Pale Orange 

Pale Pink 

Pale Purple 

Pale Turquoise 

Pale Yellow 

Paprika 

Parrot Green 

Pastel Blue 

Pastel Green 

Pastel Pink 

Pastel Purple 

Pastel Yellow 

Pea Green 

Pea Soup 

Peach 

Peachy Pink 

Peacock Blue 

Peppermint 

Peridot 

Periwinkle 

Persimmon 

Petrol 

Peuce 

Pine Green 

Pine 

Pink Lemonade 

Pink Purple 

Pink 

Pinkish Purple 

Pinkish Red 

Pinkish 

Pistachio 

Pitch Black 

Plastic Green 

Plum 

Poly Green 

Pool 

Popsicle Green 

Powder Blue 

Pretty Pink 

Primary Blue 

Primary Red 

Primrose 

Prussian Blue 

Puce 

Pumpkin 

Punch 

Pure Blue 

Purple Blue 

Purple Haze 

Purple Pink 

Purple Red 

Purple 

Purpley Pink 

Purplish Blue 

Purplish Brown 

Purplish Pink 

Purplish Red 

Putty

Rainforest 

Raspberry 

Raw Sienna 

Real Blue 

Red Brick 

Red Brown 

Red Orange 

Red Pink 

Red Purple 

Red Violet 

Red 

Reddish Brown 

Reddish Orange 

Reddish Pink 

Reddish Purple 

Reflex Blue 

Robin’s Egg Blue 

Rose Pink 

Rose Red 

Rose 

Rosy Pink 

Rouge 

Royal Blue 

Royal Purple 

Royal 

Ruby 

Russet 

Rust Brown 

Rust Orange 

Rust Red 

Rust 

Rusty Brown 

Rusty Orange 

Saffron 

Sage 

Salad 

Salmon Orange 

Salmon Pink 

Salmon 

Sand Brown 

Sand Stone 

Sand Yellow 

Sand 

Sandy Brown 

Sap Green 

Sapphire 

Scarlet 

Sea Blue 

Sea Green 

Sea Mist 

Sea 

Seafoam 

Seagreen 

Seaweed 

Sepia 

Shamrock Green 

Sherbet Green 

Shocking Green 

Shocking Pink 

Sienna 

Silver 

Sky Blue 

Sky 

Slate Blue 

Slate Grey 

Slate Green 

Slate 

Slime 

Smokey Blue 

Soft Blue 

Soft Green 

Soft Pink 

Soft Red 

Soft Rose 

Soft Yellow 

Sour Apple 

Spearmint 

Spring Green 

Spruce Green 

Squash 

Steel Blue 

Strawberry Pink 

Summer Green 

Summer Sky 

Sunburst 

Sunflower 

Sunny Yellow 

Sunshine Yellow 

Sunshine 

Swamp Green 

Tan 

Tangerine 

Taupe 

Tawny 

Teal Blue 

Teal Green 

Teal 

Terracotta 

Thistle 

Toffee 

Tomato 

Tree Green 

Tropical Green 

True Blue 

True Red 

Turf 

Turquoise Blue 

Turquoise Green 

Turquoise 

Turtle Green 

Ultramarine 

Umber 

Vanilla 

Vermillion 

Very Dark Green 

Very Dark Purple 

Vibrant Blue 

Vibrant Green 

Vibrant Purple 

Violet Blue 

Violet Purple 

Violet 

Vivid Blue 

Vivid Green 

Vivid Purple 

Vivid Violet 

Walnut 

Warm Grey 

Water Green 

Reference: MORONEY, N., 2008. The colour thesaurus. June ed. Hewlett-Packard Laboratories: Magcloud.com. 


Water 

Watermelon 

Wheat 

White 

Wine 

Wintergreen 

Wisteria 

Yellow Brown 

Yellow Green 

Yellow Ochre 

Yellow Orange 

Yellow Tan 

Yellow 

Yellowish Brown 

Yellowish Green 

Yellowish Orange 

Yellowy Green

Colour Names 

From Wikipedia List of Colours 

Air Force blue 

Alice blue 

Alizarin crimson 

Almond 

Amaranth 

Amber 

Amber (SAE/ECE) 

American rose 

Amethyst 

Android Green 

Anti-flash white 

Antique brass 

Antique fuchsia 

Antique white 

Ao (English) 

Apple green 

Apricot 

Aqua 

Aquamarine 

Army green 

Arylide yellow 

Ash grey 

Asparagus 

Atomic tangerine 

Auburn 

Aureolin 

AuroMetalSaurus 

Awesome 

Azure 

Azure mist/web 

Baby blue 

Baby blue eyes 

Baby pink 

Ball Blue 

Banana Mania 

Banana yellow 

Battleship grey 

Bazaar 

Beau blue 

Beaver 

Beige 

Bisque 

Bistre 

Bittersweet 

Black 

Blanched Almond 

Bleu de France 

Blizzard Blue 

Blond 

Blue 

Blue (Munsell) 

Blue (NCS) 

Blue (pigment) 

Blue (RYB) 

Blue Bell 

Blue Gray 

Blue-green 

Blue-violet 

Blush 

Bole 

Bondi blue 

Bone 

Boston University Red 

Bottle green 

Boysenberry 

Brandeis blue 

Brass 

Brick red 

Bright cerulean 

Bright green 

Bright lavender 

Bright maroon 

Bright pink 

Bright turquoise 

Bright ube 

Brilliant lavender 

Brilliant rose 

Brink pink 

British racing green 

Bronze 

Brown (traditional) 

Brown (web) 

Bubble gum 

Bubbles 

Buff 

Bulgarian rose 

Burgundy 

Burlywood 

Burnt orange 

Burnt sienna 

Burnt umber 

Byzantine 

Byzantium 

Cadet 

Cadet blue 

Cadet grey 

Cadmium green 

Cadmium orange 

Cadmium red 

Cadmium yellow 

Café au lait 

Café noir 

Cal Poly Pomona green 

Cambridge Blue 

Camel 

Camouflage green 

Canary yellow 

Candy apple red 

Candy pink 

Capri 

Caput mortuum 

Cardinal 

Caribbean green 

Carmine 

Carmine pink 

Carmine red 

Carnation pink 

Carnelian 

Carolina blue 

Carrot orange 

Celadon 

Celeste (colour) 

Celestial blue 

Cerise 

Cerise pink 

Cerulean 

Cerulean blue 

CG Blue 

CG Red 

Chamoisee 

Champagne 

Charcoal 

Chartreuse (traditional) 

Chartreuse (web) 

Cherry blossom pink 

Chestnut 

Chocolate (traditional) 

Chocolate (web) 

Chrome yellow 

Cinereous 

Cinnabar 

Cinnamon 

Citrine 

Classic rose 

Cobalt 

Cocoa brown 

Coffee 

Columbia blue 

Cool black 

Cool grey 

Copper 

Copper rose 

Coquelicot 

Coral 

Coral pink 

Coral red 

Cordovan 

Corn 

Cornell Red 

Cornflower blue 

Cornsilk 

Cosmic latte 

Cotton candy 

Cream 

Crimson 

Crimson glory 

Cyan 

Cyan (process) 

Daffodil 

Dandelion 

Dark blue 

Dark brown 

Dark byzantium 

Dark candy apple red 

Dark cerulean 

Dark chestnut

Dark coral 

Dark cyan 

Dark electric blue 

Dark goldenrod 

Dark gray 

Dark green 

Dark jungle green 

Dark khaki 

Dark lava 

Dark lavender 

Dark magenta 

Dark midnight blue 

Dark olive green 

Dark orange 

Dark orchid 

Dark pastel blue 

Dark pastel green 

Dark pastel purple 

Dark pastel red 

Dark pink 

Dark powder blue 

Dark raspberry 

Dark red 

Dark salmon 

Dark scarlet 

Dark sea green 

Dark sienna 

Dark slate blue 

Dark slate gray 

Dark spring green 

Dark tan 

Dark tangerine 

Dark taupe 

Dark terra cotta 

Dark turquoise 

Dark violet 

Dartmouth green 

Davy’s grey 

Debian red 

Deep carmine 

Deep carmine pink 

Deep carrot orange 

Deep cerise 

Deep champagne 

Deep chestnut 

Deep coffee 

Deep fuchsia 

Deep jungle green 

Deep lilac 

Deep magenta 

Deep peach 

Deep pink 

Deep saffron 

Deep sky blue 

Denim 

Desert 

Desert sand 

Dim gray 

Dodger blue 

Dogwood rose 

Dollar bill 

Drab 

Duke blue 

Earth yellow 

Ecru 

Eggplant 

Eggshell 

Egyptian blue 

Electric blue 

Electric crimson 

Electric cyan 

Electric green 

Electric indigo 

Electric lavender 

Electric lime 

Electric purple 

Electric ultramarine 

Electric violet 

Electric yellow 

Emerald 

Eton blue 

Fallow 

Falu red 

Fandango 

Fashion fuchsia 

Fawn 

Feldgrau 

Fern green 

Ferrari Red 

Field drab 

Firebrick 

Fire engine red 

Flame 

Flamingo pink 

Flavescent 

Flax 

Floral white 

Fluorescent orange 

Fluorescent pink 

Fluorescent yellow 

Folly 

Forest green 

(traditional) 

Forest green (web) 

French beige 

French blue 

French lilac 

French rose 

Fuchsia 

Fuchsia pink 

Fulvous 

Fuzzy Wuzzy 

Gainsboro 

Gamboge 

Ghost white 

Ginger 

Glaucous 

Glitter 

Gold (metallic) 

Gold (web) (Golden) 

Golden brown 

Golden poppy 

Golden yellow 

Goldenrod 

Granny Smith Apple 

Gray 

Gray (HTML/CSS gray) 

Gray (X11 gray) 

Gray-asparagus 

Green (color wheel) (X11 

green) 

Green (HTML/CSS 


green) 

Green (Munsell) 

Green (NCS) 

Green (pigment) 

Green 

Green-yellow 

Grullo 

Guppie green 

Halaya ube 

Han blue 

Han purple 

Hansa yellow 

Harlequin 

Harvard crimson 

Harvest Gold 

Heart Gold 

Heliotrope 

Hollywood cerise 

Honeydew 

Hooker’s green 

Hot magenta 

Hot pink 

Hunter green 

Icterine 

Inchworm 

India green 

Indian red 

Indian yellow 

Indigo (dye) 

Indigo (web) 


International orange 

Iris 

Isabelline 

Islamic green 

Ivory 

Jade 

Jasmine 

Jasper 

Jazzberry jam 

Jonquil 

June bud 

Jungle green 

Kelly green

Colour Names 


Khaki (HTML/CSS) 

(Khaki) 

Khaki (X11) (Light khaki) 

KU Crimson 

La Salle Green 

Languid lavender 

Lapis lazuli 

Laser Lemon 

Laurel green 

Lava 

Lavender (floral) 

Lavender (web) 

Lavender blue 

Lavender blush 

Lavender gray 

Lavender indigo 

Lavender magenta 

Lavender mist 

Lavender pink 

Lavender purple 

Lavender rose 

Lawn green 

Lemon 

Lemon chiffon 

Light apricot 

Light blue 

Light brown 

Light carmine pink 

Light coral 

Light cornflower blue 

Light Crimson 

Light cyan 

Light fuchsia pink 

Light goldenrod yellow 

Light gray 

Light green 

Light khaki 

Light pastel purple 

Light pink 

Light salmon 

Light salmon pink 

Light sea green 

Light sky blue 

Light slate gray 

Light taupe 

Light Thulian pink 

Light yellow 

Lilac 

Lime (color wheel) 

Lime (web) (X11 green) 

Lime green 

Lincoln green 

Linen 

Lion 

Liver 

Lust 

Magenta 

Magenta (dye) 

Magenta (process) 

Magic mint 

Magnolia 

Mahogany 

Maize 

Majorelle Blue 

Malachite 

Manatee 

Mango Tango 

Mantis 

Maroon (HTML/CSS) 

Maroon (X11) 

Mauve 

Mauve taupe 

Mauvelous 

Maya blue 

Meat brown 

Medium aquamarine 

Medium blue 

Medium candy apple 

red 

Medium carmine 

Medium champagne 

Medium electric blue 

Medium jungle green 

Medium lavender 

magenta 

Medium orchid 

Medium Persian blue 

Medium purple 

Medium red-violet 

Medium sea green 

Medium slate blue 

Medium spring bud 

Medium spring green 

Medium taupe 

Medium teal blue 

Medium turquoise 

Medium violet-red 

Melon 

Midnight blue 

Midnight green (eagle 

green) 

Mikado yellow 

Mint 

Mint cream 

Mint green 

Misty rose 

Moccasin 

Mode beige 

Moonstone blue 

Mordant red 19 

Moss green 

Mountain Meadow 

Mountbatten pink 

Mulberry 

Munsell 

Mustard 

Myrtle 

MSU Green 

Nadeshiko pink 

Napier green 

Naples yellow 

Navajo white 

Navy blue 

Neon Carrot 

Neon fuchsia 

Neon green 

Non-photo blue 

North Texas Green 

Ocean Boat Blue 

Ochre 

Office green 

Old gold 

Old lace 

Old lavender 

Old mauve 

Old rose 

Olive 

Olive Drab (web) 

Olive Drab #7 

Olivine 

Onyx 

Opera mauve 

Orange (Color Wheel) 

Orange (RYB) 

Orange (web color) 

Orange peel 

Orange-red 

Orchid 

Otter brown 

Outer Space 

Outrageous Orange 

Oxford Blue 

OU Crimson Red 

Pakistan green 

Palatinate blue 

Palatinate purple 

Pale aqua 

Pale blue 

Pale brown 

Pale carmine 

Pale cerulean 

Pale chestnut 

Pale copper 

Pale cornflower blue 

Pale gold 

Pale goldenrod 

Pale green 

Pale lavender 

Pale magenta 

Pale pink 

Pale plum 

Pale red-violet 

Pale robin egg blue 

Pale silver 

Pale spring bud 

Pale taupe

Pale violet-red 

Pansy purple 

Papaya whip 

Paris Green 

Pastel blue 

Pastel brown 

Pastel gray 

Pastel green 

Pastel magenta 

Pastel orange 

Pastel pink 

Pastel purple 

Pastel red 

Pastel violet 

Pastel yellow 

Patriarch 

Payne’s grey 

Peach 

Peach-orange 

Peach puff 

Peach-yellow 

Pear 

Pearl 

Pearl Aqua 

Peridot 

Periwinkle 

Persian blue 

Persian green 

Persian indigo 

Persian orange 

Persian pink 

Persian plum 

Persian red 

Persian rose 

Phlox 

Phthalo blue 

Phthalo green 

Piggy pink 

Pine green 

Pink 

Pink-orange 

Pink pearl 

Pink Sherbet 

Pistachio 

Platinum 

Plum (traditional) 

Plum (web) 

Portland Orange 

Powder blue (web) 

Princeton orange 

Prune 

Prussian blue 

Psychedelic purple 

Puce 

Pumpkin 

Purple (HTML/CSS) 

Purple (Munsell) 

Purple (X11) 

Purple Heart 

Purple mountain 

majesty 

Purple pizzazz 

Purple taupe 

Quartz 

Radical Red 

Raspberry 

Raspberry glace 

Raspberry pink 

Raspberry rose 

Raw umber 

Razzle dazzle rose 

Razzmatazz 

Red 

Red (Munsell) 

Red (NCS) 

Red (pigment) 

Red (RYB) 

Red-brown 

Red-violet 

Redwood 

Rich black 

Rich brilliant lavender 

Rich carmine 

Rich electric blue 

Rich lavender 

Rich lilac 

Rich maroon 

Rifle green 

Robin egg blue 

Rose 

Rose bonbon 

Rose ebony 

Rose gold 

Rose madder 

Rose pink 

Rose quartz 

Rose taupe 

Rose vale 

Rosewood 

Rosso corsa 

Rosy brown 

Royal azure 

Royal blue (traditional) 

Royal blue (web) 

Royal fuchsia 

Royal purple 

Ruby 

Ruddy 

Ruddy brown 

Ruddy pink 

Rufous 

Russet 

Rust 

Sacramento State green 

Saddle brown 

Safety orange 

Saffron 

St. Patrick’s blue 

Salmon 

Salmon pink 

Sand 

Sand dune 

Sandstorm 

Sandy brown 

Sandy taupe 

Sap green 

Sapphire 

Satin sheen gold 

Scarlet 

Scarlet (Crayola) 

School bus yellow 

Screamin’ Green 


Sea green 

Seal brown 

Seashell 

Selective yellow 

Sepia 

Shadow 

Shamrock green 

Shocking pink 

Sienna 

Silver 

Sinopia 

Skobeloff 

Sky blue 

Sky magenta 

Slate blue 

Slate gray 

Smalt (Dark powder 

blue) 

Smokey topaz 

Smoky black 

Snow 

Spiro Disco Ball 

Splashed white 

Spring bud 

Spring green 

Steel blue 

Stil de grain yellow 

Stizza 

Straw 

Sunglow 

Sunset 

Tan 

Tangelo 

Tangerine 

Tangerine yellow 

Taupe 

Taupe gray 

Tea green 

Tea rose (orange) 

Tea rose (rose) 

Teal 

Teal blue 

Teal green 

Tenné (Tawny)

Colour Names 


Terra cotta 

Thistle 

Thulian pink 

Tickle Me Pink 

Tiffany Blue 

Tiger’s eye 

Timberwolf 

Titanium yellow 

Tomato 

Toolbox 

Topaz 

Tractor red 

Trolley Grey 

Tropical rain forest 

True Blue 

Tufts Blue 

Tumbleweed 

Turkish rose 

Turquoise 

Turquoise blue 

Turquoise green 

Tuscan red 

Twilight lavender 

Tyrian purple 

UA blue 

UA red 

Ube 

UCLA Blue 

UCLA Gold 

UFO Green 

Ultramarine 

Ultramarine blue 

Ultra pink 

Umber 

United Nations blue 

University of California 

Gold 

Unmellow Yellow 

UP Forest green 

UP Maroon 

Upsdell red 

Urobilin 

USC Cardinal 

USC Gold 

Utah Crimson 

Vanilla 

Vegas gold 

Venetian red 

Verdigris 

Vermilion 

Veronica 

Violet 

Violet (color wheel) 

Violet (RYB) 

Violet (web) 

Viridian 

Vivid auburn 

Vivid burgundy 

Vivid cerise 

Vivid tangerine 

Vivid violet 

Warm black 

Wenge 

Wheat 

White 

White smoke 

Wild blue yonder 

Wild Strawberry 

Wild Watermelon 

Wine 

Wisteria 

Xanadu 

Yale Blue 

Yellow 

Yellow (Munsell) 

Yellow (NCS) 

Yellow (process) 

Yellow (RYB) 

Yellow-green 

Yellow Orange 

Zaffre 

Zinnwaldite brown 

Reference: Wikipedia, 2010. List of Colours. [online] Available at: [Accessed 09/08/10].

Names of Colours 

Alice Blue 


Antique White 

Aquamarine 

Aquamarine Medium 

Aureoline Yellow 

Azure 

Banana 

Beige 

Bisque 

Black 


Blue 

Blue Light 

Blue Medium 

Blue Violet 

Brick 

Brown 

Brown Madder 

Brown Ochre 

Burleywood 

Burnt Sienna 

Burnt Umber 

Cadet 

Cadmium Lemon 

Cadmium Orange 

Cadmium Red Deep 

Cadmium Red light 


Cadmium Yellow Light 

Carrot 

Cerulean 

Chartreuse 

Chocolate 

Chrome Oxide Green 

Cinnabar Green 

Cobalt 

Cobalt Violet Deep 

Cobalt Green 

Cold Grey 

Coral 

Coral Light 

Cornflower 

Cornsilk 

Cyan 

Cyan White 

Dark Orange 

Deep Ochre 

Deep Pink 

Dim Grey 

Dodger Blue 

Eggshell 

Emerald Green 

English Red 

Firebrick 

Flesh 

Flesh Ochre 

Floral White 

Forest Green 

Gainsboro 

Geranium Lake 

Ghost White 

Gold 

Gold Ochre 

Goldenrod 

Goldenrod Dark 

Goldenrod Light 

Goldenrod Pale 

Green 

Green Dark 

Green Pale 

Green Yellow 

Greenish Umber 

Grey 

Honeydew 

Hot Pink 

Indian Red 

Indigo 

Ivory 

Ivory Black 

Khaki 

Khaki Dark 

Lamp Black 

Lavender 

Lavender Blush 

Lawn Green 

Lemon Chiffon 

Light Beige 

Light Goldenrod 

Light Grey 

Light Salmon 

Lime Green 

Linen 

Madder Lake Deep 

Magenta 

Manganese Blue 

Maroon 

Mars Orange 

Melon 

Midnight Blue 

Mint 

Mint Cream 

Misty Rose 

Moccasin 

Naples Yellow Deep 

Navajo White 

Navy 

Old Lace 

Olive 

Olive Drab 

Olive Green Dark 

Orange 

Orange Red 

Orchid 

Orchid Dark 

Orchid Medium 

Papaya Whip 

Peach Puff 

Peacock 

Permanent Green 

Permanent Red Violet 

Peru 

Pink 

Pink Light 

Plum 

Powder Blue 

Purple 

Purple Medium 

Raspberry 

Raw Sienna 

Raw Umber 


Red 

Rose Madder 

Rosy Brown 

Royal Blue 

Saddle Brown 

Salmon 

Sandy Brown 

Sap Green 

Sea Green 

Sea Green Dark 

Sea Green Light 

Sea Green Medium 

Seashell 

Sepia 

Sienna 

Sky Blue 

Sky Blue Deep 

Sky Blue Light 

Slate Blue 

Slate Blue Dark 

Slate Blue Light 

Slate Blue Medium 

Slate Grey 

Slate Grey Dark 

Slate Grey Light 

Snow 

Spring Green 

Spring Green Medium 

Steel Blue 

Steel Blue Light 

Tan 

Terre Verte 

Thistle 


Tomato 

Turquoise 

Turquoise Blue 

Turquoise Dark 

Turquoise Medium 

Turquoise Pale 

Ultramarine 

Ultramarine Violet 

Van Dyke Brown 

Venitian Red

Colour Names 

Violet 

Violet Dark 

Violet Red 

Violet Red Medium 

Violet Red Pale 

Viridian Light 

Warm Grey 

Wheat 

White 

White Smoke 

Yellow 

Yellow Green 

Yellow Light 

Yellow Ochre 

Zinc White 

Reference: Names of Colours, 2010. [online] Available at: [Accessed 20/09/10].


Alice Blue 

Alizarin 

Amaranth 

Amber 

Amethyst 

Apricot 

Aqua 

Aquamarine 

Army Green 

Asparagus 

Auburn 

Azure 

Baby Blue 

Beige 

Bistre 

Black 

Blue 

Blue-green 

Blue-violet 

Bondi Blue 

Brass 

Bright Green 

Bright Turquoise 

Brilliant Rose 

Bronze 

Brown 

Buff 

Burgundy 

Burnt Orange 

Burnt Sienna 

Burnt Umber 

Camouflage Green 

Caput Mortuum 

Cardinal 

Carmine 

Carnation Pink 

Carolina Blue 

Carrot Orange 

Celadon 

Cerise 

Cerulean 

Cerulean Blue 

Chartreuse 

Chestnut 

Chocolate 

Cinnabar 

Cinnamon 

Cobalt 

Copper 

Copper Rose 

Coral 

Coral Red 

Corn 


Cosmic Latte 

Cream 

Crimson 

Cyan 

Dark Blue 

Dark Brown 

Dark Cerulean 

Dark Chestnut 

Dark Coral 

Dark Goldenrod 

Dark Green 

Dark Khaki 

Dark Pastel Green 

Dark Pink 

Dark Salmon 

Dark Slate Gray 

Dark Spring Green 

Dark Tan 

Dark Tangerine 


Dark Violet 

Deep Cerise 

Deep Fuchsia 

Deep Lilac 

Deep Magenta 

Deep Peach 

Deep Pink 

Denim 

Dodger Blue 

Ecru 

Electric Blue 

Electric Green 

Electric Indigo 

Electric Lime 

Electric Purple 

Emerald 

Eggplant 

Falu Red 

Fern Green 

Firebrick 

Flax 

Forest Green 

French Rose 

Fuchsia 

Fuchsia Pink 

Gamboge 

Gold 

Golden Brown 

Golden Yellow 

Goldenrod 

Gray-asparagus 

Green 

Green-yellow 

Gray 

Han Purple 

Harlequin 

Heliotrope 

Hollywood Cerise 

Hot Magenta 

Hot Pink 

Indigo 


International Orange 

Islamic Green 

Ivory 

Jade 

Kelly Green 

Khaki 

Lavender 

Lavender Blue 


Lavender Gray 

Lavender Magenta 

Lavender Pink 

Lavender Purple 

Lavender Rose 

Lawn Green 

Lemon 


Lemon Chiffon 

Light Blue 

Lilac 

Lime 

Lime Green 

Linen 

Magenta 

Malachite 

Maroon 

Maya Blue 

Mauve 

Mauve Taupe 

Medium Blue 

Medium Carmine 

Medium Purple 

Midnight Blue 

Mint Green 

Misty Rose 

Moss Green 

Mountbatten Pink 

Mustard 

Myrtle 

Navajo White 

Navy Blue 

Ochre 

Office Green 

Old Gold 

Old Lace 

Old Lavender 

Old Rose 

Olive 

Olive Drab 

Olivine 

Orange 

Orange Peel 

Orange-Red 

Orchid 

Pale Blue 

Pale Brown 

Pale Carmine 

Pale Chestnut 

Pale Cornflower Blue 

Pale Magenta 

Pale Pink

Colour Names 

Pale Red-violet 

Papaya Whip 

Pastel Green 

Pastel Pink 

Peach 

Peach-orange 

Peach-yellow 

Pear 

Periwinkle 

Persian Blue 

Persian Green 

Persian Indigo 

Persian Red 

Persian Pink 

Persian Rose 

Persimmon 

Pine Green 

Pink 

Pink-orange 

Powder Blue 

Puce 

Prussian Blue 

Psychedelic Purple 

Pumpkin 

Purple 

Purple Taupe 

Raw Umber 

Red 

Red-violet 

Rich Carmine 

Rich Magenta 

Robin Egg Blue 

Rose 

Rose Taupe 

Royal Blue 

Royal Purple 

Ruby 

Russet 

Rust 

Safety Orange 

Saffron 

Salmon 

Sandy Brown 

Sangria 

Sapphire 

Scarlet 

School Bus Yellow 

Sea Green 

Seashell 

Selective Yellow 

Sepia 

Shamrock Green 

Shocking Pink 

Silver 

Sky Blue 

Slate Gray 

Smalt 

Spring Bud 

Spring Green 

Steel Blue 

Tan 

Tangerine 

Tangerine Yellow 

Taupe 

Tea Green 

Tea Rose 

Teal 

Tenné 

Terracotta 

Thistle 

Turquoise 

Tyrian Purple 

Ultramarine 

Vermillion 

Violet 

Viridian 

Wheat 

White 

Wisteria 

Yellow 

Yellow-green 

Reference: English for students, nd. Names of colours. [online] Available at: [Accessed 07/03/11].


Alice Blue 

Antique White 

Aqua 

Aquamarine 

Azure 

Beige 

Bisque 

Black 


Blue 

Blue Violet 

Brown 

Burly Wood 

Cadet Blue 

Chartreuse 

Chocolate 

Coral 


Cornsilk 

Crimson 

Cyan 

Dark Blue 

Dark Cyan 

Dark Goldenrod 

Dark Gray 

Dark Green 

Dark Khaki 

Dark Magenta 

Dark Olive Green 

Dark Orange 

Dark Orchid 

Dark Red 

Dark Salmon 

Dark Sea Green 

Dark Slate Blue 

Dark Slate Gray 


Dark Violet 

Deep Pink 

Deep Sky Blue 

Dim Gray 

Dodger Blue 

Firebrick 

Floral White 

Forest Green 

Fuchsia 

Gainsborough 

Ghost White 

Gold 

Goldenrod 

Gray 

Green 

Green Yellow 

Honeydew 

Hot Pink 

Indian Red 

Indigo 

Ivory 

Khaki 

Lavender 


Lawn Green 

Lemon Chiffon 

Light Blue 

Light Coral 

Light Cyan 

Light Goldenrod Yellow 

Light Green 

Light Grey 

Light Pink 

Light Salmon 

Light Sea Green 

Light Sky Blue 

Light Slate Gray 

Light Steel Blue 

Light Yellow 

Lime 

Lime Green 

Linen 

Magenta 

Maroon 

Medium Aquamarine 

Medium Blue 

Medium Orchid 

Medium Purple 

Medium Sea Green 

Medium Slate Blue 

Medium Spring Green 

Medium Turquoise 

Medium Violet Red 

Midnight Blue 

Mint Cream 

Misty Rose 

Moccasin 

Navajo White 

Navy 

Old Lace 

Olive 

Olive Drab 

Orange 

Orange Red 

Orchid 

Pale Goldenrod 

Pale Green 

Pale Violet Red 

Papaya Whip 

Peach Puff 

Peru 

Pink 

Plum 

Powder Blue 

Purple 

Red 

Rosy Brown 

Royal Blue 

Saddle Brown 

Salmon 

Sandy brown 

Sea Green 

Seashell 

Sienna 

Silver 

Sky Blue 

Slate Blue 

Slate Gray 

Snow 

Spring Green 

Steel Blue 

Tan 

Teal 

Thistle 

Tomato 


Turquoise 

Violet 

Wheat 

White 

White Smoke 

Yellow 

Yellow Green 

Reference: What Colour Names are Supported in HTML, 2010. [online] Available at: [Accessed 23/09/10].

Colour Names 


Indian red 

Crimson 

Light pink 

Pink 

Pale violet red 

Lavender blush 

Hot pink 

Raspberry 

Deep pink 

Medium violet red 

Violet red 

Orchid 

Thistle 

Plum 

Violet 

Magenta 

Purple 

Medium orchid 

Dark violet 

Indigo 

Blue violet 

Medium purple 

Dark slate blue 

Light slate blue 

Medium slate blue 

Slate blue 

Ghost white 

Lavender 

Blue 

Navy 

Midnight blue 

Cobalt 

Royal blue 

Cornflower blue 

Light steel blue 

Light slate gray 

Slate gray 

Dodger blue 

Alice blue 

Steel blue 

Light sky blue 

Sky blue 

Deep sky blue 

Peacock 

Light blue 

Powder blue 

Cadet blue 

Turquoise 

Dark turquoise 

Azure 

Light cyan 

Pale turquoise 

Dark slate gray 

Cyan 

Teal 

Medium turquoise 

Manganese blue 

Cold grey 

Turquoise blue 

Aquamarine 

Medium spring green 

Mint cream 

Spring green 

Medium sea green 

Sea green 

Emerald green 

Mint 

Cobalt green 

Honey dew 

Dark sea green 

Pale green 

Lime green 

Forest green 

Green 

Dark green 

Sap green 

Lawn green 

Chartreuse 

Green yellow 

Dark olive green 

Olive drab 

Ivory 

Beige 

Light yellow 

Light goldenrod yellow 

Yellow 

Warm grey 

Olive 

Dark khaki 

Khaki 

Pale goldenrod 

Lemon chiffon 

Light goldenrod 

Banana 

Gold 

Cornsilk 

Goldenrod 

Dark goldenrod 

Floral white 

Old lace 

Wheat 

Moccasin 

Papaya whip 

Blanched almond 

Navajo white 

Eggshell 

Tan 

Brick 

Cadmium yellow 

Antique white 

Burlywood 

Bisque 

Melon 

Carrot 

Dark orange 

Orange 

Linen 

Peach puff 

Seashell 

Sandy brown 

Raw sienna 

Chocolate 

Ivory black 

Flesh 

Cadmium orange 

Burnt sienna 

Sienna 

Reference: 500+ Colours, 2010. [online] Available at: [Accessed 23/09/10]. 

Light salmon 

Orange red 

Sepia 

Dark salmon 

Coral 

Burnt umber 

Tomato 

Salmon 

Misty rose 

Snow 

Rosy brown 

Light coral 

Brown 

Firebrick 

Red 

Maroon 

White 

White smoke 

Gainsboro 

Light grey 

Silver 

Dark gray 

Gray 

Black 

Dim gray

Names of Colours from the Maerz and Paul Dictionary of Colour 

veronese green 

abbey 

absinthe [green] 

absinthe yellow 

acacia 

academy blue 

acajou 

acanthe 

acier 

ackermann’s green 

aconite violet 

acorn 

adamia 

adelaide 

aden 

admiral 

adobe 

adrianople red 

adriatic 

adust 

aero 

afghan 

afghan red 

african 

african brown 

afterglow 

agate 

agate grey 

ageratum blue 

ageratum violet 

air blue 

airedale 

airway 

akbar 

alabaster 

alamo 

alcanna 

alcazar 

alderney 

alesan 

alexandria blue 

alfalfa 

algerian 

algerian red 

algonquin 

alhambra 

alice blue 

alkermes 

almond 

almond [pink] 

almond blossom 

almond brown 

almond green 

aloes [green] 

aloma 

alpine 

alpine green 

aluminum 

amaranth 

amaranth pink 

amaranth purple 

amaranthine 

amarna 

amarylis 

amazon 

amber [yellow] 

amber brown 

amber white 

amberglow 

ambrosia 

ambulance 

amelie 

american beauty 

american green 

amethyst [violet] 

amparo blue 

amparo purple 

amulet 

anamite 

anatolia 

anatta 

andorra 

andorre 

andover green 

andrinople berries 

anemone 

angel red 

animal rouge 

annapolis 

annatto 

annotto 

antelope 

antimony yellow 

antique 

antique brass 

antique bronze 

antique brown 

antique drab 

antique fuchsia 

antique gold 

antique green 

antique red 

antique ruby 

antwerp blue 

antwerp brown 

antwerp red 

apache 

aphrodite 

apple green 

apple-fallow 

appleblossom 

apricot 

apricot yellow 

aquagreen 

aquamarine 

arab [brown] 

arabesque 

arabian brown 

arabian red 

araby 

aragon 

arbutus 

arcadian green 

archel 

archil 

arctic 

arctic blue 

ardoise 

argali 

argent 

argentina 

argus brown 

argyle purple 

arizona 

armada 

armenian blue 

armenian bole 

armenian red 

armenian stone 

army brown 

arno blue 

arnotto 

arona 

arrowwood 

arsenate 

art brown 

art gray 

art green 

artemesia green 

artichoke green 

artificial vermilion 

artillery 

ascot tan 

ash [grey] 

ashes of rose 

asmalte 

asparagus green 

aspen green 

asphalt 

asphaltum 

asphodel green 

aster 

aster purple 

athenia 

atlantic 

atlantis 

atmosphere 

atramentous 

attar of roses 

aubergine 

auburn 

aubusson 

aucuba 

aureolin 

auricula purple 

auripigmentum 

aurora [orange] 

aurora red 

aurora yellow 

aurore 

aurum 

australian pine 

auteuil 

autumn 

autumn blonde 

autumn brown 

autumn glory 

autumn green 

autumn leaf 

autumn oak 

avellaneous 

avignon berries 


azalea 

aztec 

aztec maroon 

azulin 

baby blue 

baby pink 

baby rose 

bacchus 

bachelor button 

bagdad 

bakst green 

balge yellow 

ball lake 

balsam 

balsam green 

baltic 

bambino 

bamboo 

banana 

banner 

banshee 

barberry 

bark 

barwood 

basketball 

bastard saffron 

bat 

bath stone 

battleship grey 

bay 

bayou 

beach 

beach tan 

bear 

bear’s hair 

bearskin [grey] 

beaucaire 

beaver 

beaverpelt 

beech 

beechnut 

beef’s blood 

beeswax 

beetle 

begonia 

begonia rose 

beige 

beige soiree 

belladonna 

belleek 

bellemonte 

bellflower 

berlin brown 

berlin red 

bermuda 

beryl 

beryl blue 

beryl green 

biarritz 

big four yellow 

billiard 

biscay green 

biscuit 

bishop 

bishop’s purple 

bishop’s violet 

biskra 

bismarck 

bismarck brown 

bison 

bisque 

bister 

bistre 

bistre green 

bittersweet 

bittersweet orange 

bittersweet pink 

bitumen 

bladder green 

blaze 

bleu d’orient 

bleu de lyons 

bleu louise 

bleu passe 

blond 

blondine 

blonket 

blood red 

blossom 

blue ashes 

blue aster 

blue bice 

blue bird 

blue devil 

blue fox 

blue grass

Colour Names 


blue haze 

blue jewel 

blue lavender 

blue lotus 

blue ochre 

blue orchid 

blue sapphire 

blue spruce 

blue turquoise 

blue untramarine ash 

blue verditer 

bluebell 

bluejay 

bluet 

blunket 

blush 

blush rose 

boa 

bobolink 

bois de rose 

bokhara 

bole 

bole armoniack 

bolus 

bombay 

bone brown 

bonfire 

bonito 

bonnie blue 

boreal 

bosphorus 

botticelli 

bottle green 

bougainville 

boulevard 

bouquet green 

bourgeon 

box green 

bracken 

bramble 

bran 

brass 

brazen yellow 

brazil [red] 

brazil brown 

brazilwood 

bremen blue 

bremen green 

brewster green 

briar 

briarwood 

brick red 

brickdust 

bridal rose 

brigand 

brimstone [yellow] 

brittany 

broccoli brown 

broncho 

bronze 

bronze brown 

bronze clair 

bronze green 

bronze lustre 

bronze nude 

bronze red 

bronze yellow 

bronzesheen 

brown bay 

brown bread 

brown madder 

brown ochre 

brown pink 

brown red 

brown stone 

brown sugar 

brownstone 

bruges 

brushwood 

brussels brown 

buccaneer 

buccaneer 

buckskin 

buckthorn berries 

buckthorn brown 

bud green 

buddha 

buff 

buffalo 

bulgare 

bunny 

bure 

burel 

burgundy 

burgundy violet 

burlwood 

burma 

burmese gold 

burmese ruby 

burn blue 

burnished gold 

burnous 

burnt 

burnt almond 

burnt carmine 

burnt coral 

burnt crimson lake 

burnt italian earth 

burnt italian ochre 

burnt lake 

burnt ochre 

burnt orange 

burnt roman ochre 

burnt rose 

burnt russet 

burnt sienna 

burnt terre verte 

burnt umber 

buttercup [yellow] 

butterfly 

butternut 

butterscotch 

byron 

byzantine 

byzantium 

cabaret 

cabbage green 

cacao 

cacao brown 

cacha 

cachou 

cactus 

cadet 

cadet blue 

cadet grey 

cadmium carmine 

cadmium green 

cadmium lemon 

cadmium orange 

cadmium purple 

cadmium vermilion 

cadmium yellow 

caen stone 

caeruleum 

cafe creme 

cafe noir 

cafe-au-lait 

cairo 

calabash 

calamine blue 

caldera 

caledonian brown 

caliaturwood 

california color 

california green 

calla green 

calliste green 

cambridge blue 

cambridge red 

camel 

camellia 

camel’s hair 

cameo blue 

cameo brown 

cameo green 

cameo pink 

cameo yellow 

campanula 

campanula blue 

campanula purple 

campanula violet 

camwood 

camerier 

canard 

canary [yellow] 

canary green 

canary-bird green 

candida 

candy pink 

canna 

cannon 

canterbury 

canton [blue] 

canton jade 

canyon 

cappagh 

cappah brown 

capri 

caprice 

capucine 

capucine buff 

capucine lake 

capucine madder 

capucine orange 

capucine red 

capucine yellow 

caput mortuum 

caraibe 

caramel 

carbuncle 

cardinal [red] 

carmine 

carmine lake 

carnation 

carnation [red] 

carnation rose 

carnelian 

carnelian red 

carnival red 

carob brown 

caroubier 

carrara green 

carrot red 

carthamus red 

carthamus rose 

cartouche 

cartridge buff 

cascade 

cashew 

cashew lake 

cashew nut 

cashoo 

casino pink 

cassel brown 

cassel earth 

cassel yellow 

casserole 

castaneous 

castellon 

castile earth 

castilian brown 

castilian red 

castor 

castor grey 

catalpa 

catawba 

cate 

catechu 

cathay 

cathedral 

cathedral blue 

cattail 

cattleya 

caucasia 

cauldron 

cavalry 

cedar 

cedar green 

cedar wood 

cedarbark 

cedre 

celadon green 

celandine green 

celest 

celestial 

celestial blue 

cellini 

cement 

cendrillon 

centennial brown 

centre blue 

ceramic 

ceres 

cerise 

cerro green 

certosa 

cerulean 

cerulean blue 

ceylon blue 

ch’ing 

chaetura black 

chaetura drab 

chalcedony yellow 

chalet red 

chamois 

chamois skin 

chamoline 

champagne 

chantilly 

charcoal grey 

chardon 

charles x 

chartreuse 

chartreuse green 

chartreuse yellow 

chasseur 

chatemuc 

chatenay pink

chaudron 

chaudron 

checkerberry 

chemic blue 

chemic green 

cherokee 

cherry 

cherry bloom 

cherry blossom 

cherry red 

cherub 

chessylite blue 

chestnut 

chevreuse 

chianti 

chickadee gray 

chicle 

chicory 

chicory blue 

chimney red 

chin-chin blue 

china blue 

china rose 

chinchilla 

chinese gold 

chinese green 

chinese lake 

chinese orange 

chinese red 

chinese rouge 

chinese vermilion 

chinese violet 

chinese yellow 

chinook 

chip 

chipmunk 

chippendale 

chiswick 

chocolate 

chocolate brown 

chocolate maroon 

chrome citron 

chrome green 

chrome lemon 

chrome orange 

chrome primrose 

chrome scarlet 

chromium green 

chromium oxide 

chrysanthemum 

chrysocollo 

chrysolite green 

chrysopraise 

chukker brown 

chutney 

cigarette 

cinder [grey] 

cineraria 

cinereous 

cinnamon 

cinnamon pink 

circassian 

citron [yellow] 

citron green 

citronelle 

citrus 

civette green 

clair de lune 

claret [red] 

claret cup 

claret lees 

claver 

clay 

clay bank 

clay drab 

clematis 

cleopatra 

clochette 

cloisonne 

cloud 

cloud grey 

cloudy amber 

clove 

clove brown 

clove pink 

clover 

cobalt blue 

cobalt glass 

cobalt green 

cobalt red 

cobalt ultramarine 

cobalt violet 

cobalt yellow 

cobblestone 

cobweb 

coccineous 

cocher 

cochin 

cochineal 

cockatoo 

cocoa 

cocoa brown 

cocobala 

coconut 

coconut brown 

cod grey 

coeruleum 

coffee 

cognac 

colcothar 

colewort green 

colibri 

collie 

cologne brown 

cologne earth 

cologne yellow 

colonial 

colonial buff 

colonial yellow 

columbia 

columbia blue 

columbian red 

columbine blue 

columbine red 

comet 

commelina blue 

commodore 

como 

conch shell 

concord 

condor 

confetti 

congo [brown] 

congo pink 

continental blue 

cookie 

coolie 

copenhagen [blue] 

copper 

copper blue 

copper brown 

copper green 

copper lustre 

copper red 

copper rose 

copper yellow 

copperleaf 

copra 

coptic 

coquelicot 

coquette 

coral [red] 

coral blush 

coral pink 

coral sands 

coralbell 

corcir 

cordova 

cordovan 

corial 

corinth 

corinthian pink 

corinthian purple 

corinthian red 

cork 

corker 

corkur 

corn 

cornelian red 

cornflower [blue] 

cornhusk 

cornsilk 

coromandel 

coronation 

corsage green 

corsair 

corsican blue 

corydalis green 

cosmos 

cossack 

cossack green 

cosse green 

cotch 

cotinga purple 

cotrine 

courge green 

cowboy 

cowslip 

crabapple 

cracker 

crag 

crane 


crash 

crawshay red 

crayon blue 

cream 

cream beige 

cream buff 

creole 

cress green 

cresson 

crevette 

crimson lake 

crimson madder 

crimson maple 

croceus 

crocreal 

crocus 

crocus martis 

crostil 

crotal 

crottle 

crowshay red 

cruiser 

crushed berry 

crushed strawberry 

crushed violets 

crystal grey 

crystal palace blue 

crystal palace green 

crystals of venus 

cub 

cuba 

cuban sand 

cudbear 

cuir 

cuisse de nymphe 

cullen earth 

cupid pink 

cupreous 

curcuma 

currant [red] 

cutch 

cuyahoga red 

cyan blue 

cyanine blue 

cyclamen 

cygnet 

cypress [green] 

cypress green 

cyprus earth 

cyprus umber 

daffodil yellow 

daffodile [yellow] 

dagestan 

dahlia 

dahlia carmine 

dahlia mauve 

dahlia purple 

damascen 

damask 

damonico 

damonico 

damson 

danae 

dandelion 

dante 

danube green 

daphne 

daphne pink 

daphne red 

dark beaver 

dark cardinal 

dark gobelin blue 

dark wedgwood [blue] 

date 

datura 

dauphine 

davy’s grey 

dawn 

dawn grey 

daybreak 

daytona 

de’medici 

dead carnations 

dead leaf 

deauville sand 

deep brunswick green 

deep chrome green 

deep chrome yellow 

deep stone 

deer 

del monte 

delft blue 

della robbia 

delphinium 

denmark 

denver

Colour Names 


derby blue 

desert 

devil’s red 

devon [brown] 

dewey red 

dewkiss 

di palito 

diadem 

diana 

dianthus 

diavolo 

digitalis 

directoire blue 

directoire blue 

distilled green 

diva blue 

doe 

doe-skin brown 

doge 

dogwood 

dolly pink 

domingo 

domingo brown 

dorado 

doubloon 

dove 

dove grey 

dover grey 

dozer 

drab 

drabolive 

dragon’s blood 

dragonfly 

drake 

drake’s-neck green 

dregs of wine 

dresden blue 

dresden brown 

dry rose 

dryad 

du barry 

du gueslin 

duck blue 

duck green 

duck-wing 

duckling 

dumont’s blue 

dune 

durango 

dusk 

dust 

dusty green 

dutch azure 

dutch blue 

dutch orange 

dutch pink 

dutch scarlet 

dutch vermilion 

dutch ware blue 

dyer’s broom 

dyer’s greenwood 

dyer’s saffron 

debutante pink 

eagle 

eau-de-javel green 

eau-de-nile 

eburnean 

ecclesiastic 

ecru 

eden green 

eggplant 

eggshell blue 

eggshell green 

eglantine 

egypt 

egyptian 

egyptian blue 

egyptian brown 

egyptian green 

egyptian red 

eifel 

elderberry 

eldorado 

electric [blue] 

electric green 

elephant green 

elephant skin 

elf 

elfin green 

elk 

elm green 

elmwood 

email 

ember 

emberglow 

emerald green 

emeraude 

eminence 

emperor green 

empire 

empire blue 

empire green 

empire yellow 

enamel blue 

endive 

endive blue 

english blue 

english green 

english grey 

english inde 

english ivy 

english oak 

english ochre 

english pink 

english red 

english vermilion 

english violet 

ensign 

epinauche 

epsom 

erin 

erlau green 

escadre 

eschel 

eskimo 

esthetic grey 

etain blue 

etang 

ether 

ethereal blue 

eton blue 

etruscan 

etruscan red 

eucalyptus [green] 

eugenia red 

eugenie 

eupatorium purple 

eureka red 

eve green 

evenglow 

eventide 

everglade 

evergreen 

eveque 

faded rose 

faience 

fairway 

fairy green 

fakir 

falcon 

fallow 

fandango 

faon 

fashion gray 

fawn [brown] 

feldspar 

fern 

fern green 

fernambucowood 

ferruginous 

feuille 

feuille morte 

feulamort 

fez 

field’s orange 

vermilion 

fiery red 

fiesta 

fieulamort 

filbert [brown] 

filemot 

fillemot 

fir [green] 

fire red 

fire scarlet 

firecracker 

firefly 

fireweed 

firmament 

firmament blue 

fish grey 

flag 

flame [scarlet] 

flame blue 

flame orange 

flaming maple 

flamingo 

flammeous 

flash 

flax 

flaxen 

flaxflower blossom 

flaxflower blue 

flea 

flemish blue 

flesh 

flesh blond 

flesh ochre 

fleur-de-lys 

flint 

flint grey 

flirt 

florence brown 

florence earth 

florentine 

florentine lake 

florida gold 

floss flower blue 

flower de luce green 

fluorite green 

fluorite violet 

fog 

foliage brown 

foliage green 

folimort 

folkstone 

folly 

fontainebleau 

forest 

forest green 

forest of dean red 

forget-me-not [blue] 

formosa 

forsythia 

fox 

fox trot 

foxglove 

fragonard 

france 

france rose 

freedom 

freestone 

french beige 

french berries 

french blue 

french green 

french grey 

french lilac 

french maroon 

french nude 

french ochre 

french pink 

french purple 

french scarlet 

french ultramarine 

french vermilion 

french white 

french yellow 

friar 

frost grey 

frosty green 

fuchsia 

fudge 

fujiyama 

fuscous 

fustet 

fustic 

gage green 

gaiety 

galleon 

gallstone 

gambia 

gamboge 

gardenia green 

gargoyle 

garland green 

garnet 

garnet brown 

garnet red 

garter blue 

gaude 

gaude lake 

gaudy green 

gay green 

gazelle [brown] 

geisha 

gendarme [blue] 

generall 

genestrole 

genet 

geneva blue 

genista 

genoa blue 

gentian 

gentian blue 

genuine ultramarine 

geranium 

geranium lake

geranium petal 

geranium pink 

ghent 

giallolini 

giallolino 

gigas 

gild 

gilded 

gilt 

gingeline 

gingeoline 

ginger 

gingerline 

gingerspice 

gingioline 

giraffe 

glacier 

glacier blue 

gladiolus 

glass green 

glass grey 

glaucous 

glaucous-blue 

glaucous-green 

glaucous-grey 

glaeeul 

glint o’gold 

gloaming 

glory 

gloxinia 

gmelin’s blue 

gnaphalium green 

goat 

gobelin blue 

gobelin green 

gobelin scarlet 

gold 

gold bronze 

gold brown 

gold earth 

gold leaf 

gold ochre 

gold yellow 

golden 

golden brown 

golden chestnut 

golden coral 

golden corn 

golden feather 

golden glow 

golden green 

golden ochre 

golden poppy 

golden rod 

golden wheat 

golden yellow 

golf [red] 

golf green 

goose grey 

gooseberry 

gooseberry green 

gorevan 

goura 

goya 

grain 

grain in grain 

granada 

granat 

granatflower 

granatflower 

granite 

granite blue 

grape 

grape blue 

grape green 

grapefruit 

grapejuice 

grapenuts 

graphite 

grass green 

grasshopper 

gravel 

grayn 

grebe 

grecian rose 

green ash 

green finch 

green slate 

green stone 

grenadine red 

grenat 

gretna green 

grey 31 

grey dawn 

grey drab 

grey stone 

grey ultramarine ash 

greyn 

gridelin 

griffin 

gris-de-lin 

grotto [blue] 

grouse 

guignet’s green 

guimet’s blue 

guinea green 

guinea hen 

gull 

gull grey 

gunmetal 

gypsy 

gypsy red 

haematite red 

hair brown 

hamadan 

hamburg lake 

hampstead brown 

hankow 

hankow 

harbor blue 

harlem blue 

harlequin 

harrison red 

harvard crimson 

harvest 

hathi gray 

hathor 

havana rose 

hay 

hazel 

hazelnut 

hazy blue 

heather 

hebe 

heliotrope 

heliotrope grey 

hellebore green 

hellebore red 

helvetia blue 

hemlock 

hemp 

henna 

hepatica 

hermosa pink 

heron 

hibernian green 

highland green 

hindu 

hispano 

hockey 

holland blue 

holly berry 

holly green 

hollyhock 

hollywood 

homage blue 

honey [yellow] 

honey beige 

honey bird 

honeydew 

honeysuckle 

honeysweet 

hopi 

horace vernet’s blue 

horizon [blue] 

horsechestnut 

hortense violet 

hortensia 

huckleberry 

hudson seal 

hummingbird 

hungarian blue 

hungarian green 

hunter [green] 

hunter’s green 

huron 

hussar 

hyacinth 

hyacinth blue 

hyacinth red 

hyacinth violet 

hydrangea blue 

hydrangea pink 

hydrangea red 

hydro 

hypermic red 

hyssop violet 

ibis pink 

ibis red 

iceberg 

immenssee 

imperial 


imperial blue 

imperial green 

imperial jade 

imperial purple 

imperial stone 

imperial yellow 

inca gold 

inde blue 

indebaudias 

independence 

india ink 

india red 

india spice 

india tan 

indian 

indian blue 

indian brown 

indian buff 

indian lake 

indian orange 

indian pink 

indian purple 

indian red 

indian saffron 

indian tan 

indian turquoise 

indian yellow 

indiana 

indico 

indico carmine 

indigo 

indigo carmine 

indigo extract 

indo 

infanta 

infantry 

infernal blue 

ingenue 

ink black 

ink blue 

intense blue 

international 

invisible green 

ionian blue 

iris 

iris green 

iris mauve 

irisglow 

iron 

iron blue 

iron brown 

iron buff 

iron crocus 

iron grey 

iron minium 

iron oxide red 

iron red 

iron saffron 

iron yellow 

isabella 

ispahan 

italian blue 

italian lake 

italian ochre 

italian pink 

italian straw 

ivory [yellow] 

ivory brown 

ivory white 

ivy [green] 

jacaranda brown 

jacinthe 

jack rabbit 

jack rose 

jacqueminot 

jadesheen 

jaffa orange 

jaffi 

jalapa 

japan blue 

japan earth 

japan rose 

japanese blue 

japanese green 

japanese red 

japanese yellow 

japonica 

jasmine 

jasper 

jasper green 

jasper pink 

jasper red 

java 

java brown 

javel green 

jay [blue]

Colour Names 


jean bart 

jew’s pitch 

jockey 

jonquil [yellow] 

josephine 

jouvence blue 

judee 

jungle green 

juniper 

kabistan 

kaffa 

kaiser brown 

kangaroo 

kara 

kara dagh 

kasha-beige 

kashan 

kashmir 

kazak 

kentucky green 

kermanshah 

kermes 

kermes berries 

kesseler yellow 

kettledrum 

khaki 

khaki 

khiva 

kildare green 

killarney [green] 

kinema red 

king’s blue 

king’s yellow 

kingfisher 

kirchberger green 

kis kilim 

kobe 

kolinsky 

korea 

kork 

korkir 

kremlin 

kronberg’s green 

kurdistan 

kyoto 

la france pink 

la valliere 

labrador 

lac lake 

lacquer red 

laelia pink 

lagoon 

lake 

lake blue 

lama 

lambert’s blue 

lapis 

lariat 

lark 

larkspur 

latericeous 

lateritious 

latoun 

latten 

laundry blue 

laurel green 

laurel oak 

laurel pink 

lava 

lavender 

lawn green 

lead 

lead grey 

lead ochre 

leadville 

leaf mold 

leaf red 

leather 

leather brown 

leather lake 

leek green 

leghorn 

legion blue 

leitch’s blue 

leithner’s blue 

lemmian earth 

lemnian ruddle 

lemnos 

lemon chrome 

lemon yellow 

lettuce green 

levant red 

leyden blue 

liberia 

liberty 

liberty blue 

liberty green 

lichen 

lichen green 

lido 

lierre 

light blue 

light brunswick green 

light chrome green 

light chrome yellow 

light grege 

light gunmetal 

light red 

light stone 

light wedgwood 

[blue] 

lilac 

lilac gray 

lilaceous 

lilas 

lilium 

lily green 

limawood 

lime [yellow] 

lime blue 

lime green 

limestone 

limoges 

lincoln green 

lincoln red 

linden green 

linden yellow 

linoleum brown 

lint 

lint-white 

lion 

lion tawny 

liqueur green 

liseran purple 

litho purple 

liver 

liver brown 

liver maroon 

livid 

livid brown 

livid pink 

livid purple 

livid violet 

lizard [green] 

lizard bronze 

lo-kao 

loam 

lobelia [blue] 

lobelia violet 

lobster 

locarno green 

log cabin 

loganberry 

logwood 

logwood blue 

london brown 

london smoke 

long beach 

longchamps 

lotus 

louis philippe 

loutre 

louvain 

love bird 

love-in-a-mist 

lucerne blue 

luciole 

lucky stone 

lumiere blue 

lumiere green 

lupine 

luteous 

luxor 

lyons blue 

macaroon 

madder 

madder blue 

madder brown 

madder carmine 

madder indian red 

madder lake 

madder pink 

madder red 

madder violet 

madeline blue 

madonna 

madrid 

madura 

magenta 

magenta rose 

magnolia 

mahogany 

mahogany brown 

mahogany red 

maiden’s blush 

maintenon 

maise 

majolica 

majolica blue 

majolica earth 

malabar 

malacca 

malachite green 

malaga 

malay 

mallard 

mallow pink 

mallow purple 

mallow red 

malmaison 

malmaison rose 

manchu 

mandalay 

mandarin orange 

mandarin red 

manganese brown 

manganese velvet 

brown 

manganese violet 

mango 

manila 

manon 

manzanita 

maple 

maple sugar 

maracaibo 

marathon 

marble green 

marguerite yellow 

marie antoinette 

marine [blue] 

marine corps 

marine green 

maris 

marmora 

marocain 

marone 

maroon 

marron 

marron glace 

marrone 

mars brown 

mars orange 

mars red 

mars violet 

mars yellow 

marsala 

marsh rose 

martinique 

martius yellow 

marygold 

mascara 

mascot 

massicot [yellow] 

mast colour 

mastic 

matelot 

matrix 

mauve blush 

mauve castor 

mauve dust 

mauve taupe 

mauveglow 

mauverose 

mauvette 

mauvewood 

mavis 

maya 

mayfair tan 

mayflower 

mazarine blue 

meadow [green] 

meadow violet 

meadow violet 

meadowbrook 

meadowgrass 

meadowlark 

meadowpink 

meadowsweet 

mecca 

medal bronze 

medici blue 

mediterranian 

medium chrome 

green 

meerschaum 

mehal 

melilot

meline 

mello-mauve 

mellowglow 

melon 

mephisto 

merida 

merle 

mermaid 

mesa 

metal 

metal brown 

metallic green 

metallic grey 

mexican 

mexican red 

mexico 

miami sand 

michigan 

microcline green 

middle brunswick 

green 

middle stone 

middy 

midnight 

midnight sun 

mignon 

mignon green 

mignonette [green] 

mikado 

mikado brown 

mikado orange 

milano blue 

milk white 

milwaukee brick 

mimosa 

mindoro 

mineral bister 

mineral blue 

mineral green 

mineral grey 

mineral orange 

mineral pitch 

mineral purple 

mineral red 

mineral rouge 

mineral violet 

mineral yellow 

ming green 

miniature pink 

minium 

mint 

minuet 

mirabelle 

mirador 

mirage 

mist [grey] 

mist blue 

mistletoe 

misty blue 

misty morn 

mitis green 

mittler’s green 

moccasin 

mocha 

mocha bisque 

mode beige 

mohawk 

monaco 

monet blue 

monicon 

monkey skin 

monkshood 

monsignor 

monsoreau 

monte carlo 

montella 

monterey 

monticello green 

montpellier green 

montpellier yellow 

moonbeam 

moonlight 

moonlight blue 

moonmist 

moorish red 

moose 

mordore 

morea berries 

morello 

moresco 

morillon 

morning blue 

morning dawning 

yellow 

morning glory 

moro red 

moroccan 

morocco 

morocco red 

morocco sand 

morro 

mort d’ore 

mosaic blue 

mosque 

moss [green] 

moss grey 

moss pink 

moss rose 

mosul 

moth [grey] 

motmot blue 

motmot green 

mount vernon green 

mountain blue 

mountain green 

mountain yellow 

mouse [grey] 

mouse-dun 

mousse 

muffin 

mulberry 

mulberry fruit 

mulberry purple 

mummy 

mummy brown 

muraille 

murillo 

murinus 

murrey 

muscade 

muscovite 

mushroom 

musk 

musketeer 

muskmelon 

muskrat 

mustang 

mustard [yellow] 

mustard brown 

mutrie yellow 

myosotis blue 

myrtle [green] 

mytho green 

mesange 

metallique 

nacarat 

nacarine 

naiad 

nankeen [yellow] 

naples red 

naples yellow 

napoleon blue 

napoli 

narcissus 

narrawood 

narva 

nasturtium [red] 

nasturtium [yellow] 

natal brown 

national [blue] 

national grey 

nattier 

natural 

navaho 

navy 

navy blue 

neapolitan blue 

neapolitan yellow 

nectar 

nectarine 

negro 

neptune [green] 

neutral orange 

neutral red 

neutral tint 

neuvider green 

neuwied blue 

neuwieder blue 

neuwieder green 

neva green 

new blue 

new bronze 

new cocoa 

new hay 

new silver 

newport 

niagara 

niagara green 

nicaraguawood 

nice 

nightshade 

nikko 


nile [green] 

nile blue 

nimbus 

nippon 

noisette 

nomad brown 

norfolk 

normandy 

normandy blue 

nougat 

nubian 

nude 

nugget 

nuncio 

nuremberg red 

nuremberg violet 

nutmeg 

nutria 

nymph [pink] 

nymph green 

nymphea 

oad 

oak [brown] 

oak [green] 

oakbuff 

oakheart 

oakleaf brown 

oakwood 

oasis 

ocean green 

ocher red 

ochraceous 

ochre 

ochre de ru 

ochre red 

oil blue 

oil green 

oil yellow 

oker de luce 

oker de luke 

oker de rouse 

oker de rouse 

old amethyst 

old blue 

old bronze 

old burgundy 

old cedar 

old china 

old coral 

old english brown 

old gold 

old helio 

old ivory 

old lavender 

old lilac 

old mauve 

old moss [green] 

old olive 

old pink 

old red 

old rose 

old roseleaf 

old silver 

old wood 

olde 

olivaceous 

olive [green] 

olive brown 

olive drab 

olive grey 

olive terra verte 

olive wood 

olive yellow 

olivesheen 

olivine 

olympia 

olympian blue 

olympian green 

olympic 

olympic blue 

ondine 

onion red 

onion-peel 

onion-skin pink 

ontario violet 

oold 

opal 

opal blue 

opal grey 

opal mauve 

opaline green 

opaq 

opera mauve 

opera pink 

ophelia 

oporto

Colour Names 


orange aurora 

orange lead 

orange madder 

orange ochre 

orange rufous 

orange tawny 

orange vermilion 

orange-peel 

orchadee 

orchall 

orchid 

orchid pink 

orchil 

orchis 

orient 

orient [blue] 

orient blue 

orient pink 

orient red 

orient yellow 

oriental 

oriental blue 

oriental bole 

oriental fuchsia 

oriental green 

oriental pearl 

oriental red 

oriole 

orion 

orlean 

ormond 

orpiment 

orpiment orange 

orpiment red 

orpin 

orseille 

otter [brown] 

oural green 

owl 

oxblood [red] 

oxford blue 

oxford chrome 

oxford ochre 

oxford yellow 

oxgall 

oxheart 

oxide blue 

oxide brown 

oxide purple 

oxide yellow 

oyster [white] 

oyster grey 

pablo 

pacific 

paddock 

pagoda [blue] 

palestine 

palm 

palm green 

palmetto 

palmleaf 

paloma 

pampas 

pancy green 

pansy 

pansy purple 

pansy violet 

pansy-maroon 

pansee 

paon 

paper white 

paprica 

papyrus 

para red 

paradise green 

parchment 

paris green 

paris mud 

paris red 

paris yellow 

park green 

parma red 

parme 

parrakeet 

parroquit green 

parrot green 

partridge 

parula blue 

pastel 

pastel blue 

pastel grey 

pastel parchment 

patent yellow 

patina green 

patriarch 

patricia 

paul veronese green 

pauncy green 

pavonine 

pawnee 

payne’s grey 

pea green 

peach 

peach bisque 

peach bloom 

peach blossom [pink] 

peach blossom [red] 

peach blow 

peach blush 

peach red 

peachbeige 

peachwood 

peacock [blue] 

peacock green 

peanut 

pearl 

pearl blue 

pearl gray 

pearl white 

pearlblush 

peasant 

peasant blue 

pebble 

pecan 

pecan brown 

pekinese 

peking blue 

pelican 

peligot’s blue 

pelt 

pencilwood 

penguin 

peony 

peony red 

pepita 

pepper red 

peppermint 

peridot 

perilla 

perilla purple 

perique 

periwinkle 

perma red 

permanent blue 

permanent red 

permanent violet 

permanent yellow 

pernambucowood 

perruche 

persenche 

persian blue 

persian earth 

persian green 

persian lilac 

persian melon 

persian orange 

persian pink 

persian red 

persian rose 

persian yellow 

persimmon 

persio 

persis 

peruvian brown 

peruvian yellow 

pervenche 

petunia 

petunia [violet] 

pewke 

pewter 

phantom 

phantom red 

pharaoh 

pheasant 

philamot 

phlox 

phlox pink 

phlox purple 

phyliamort 

pi yu 

piccadilly 

piccaninny 

piccolpasso red 

pigeon 

pigeon blood 

pigeon neck 

pigeon’s throat 

pigeon’s-breast 

pigskin 

pilgrim 

pilgrim brown 

pilot blue 

pimento 

pimlico 

pinard yellow 

pinchbeck brown 

pine frost 

pine tree 

pineapple 

pinecone 

pinegrove 

pineneedle 

pink 

pink coral 

pink pearl 

piping rock 

piquant green 

pirate 

pistache 

pistachio green 

pitchpine 

piuree 

piuri 

plantation 

platina yellow 

platinum 

plaza grey 

pleroma violet 

plover 

plum [purple] 

plum purple 

plum violet 

plumbaceous 

plumbago blue 

plumbago grey 

plumbago slate 

plumbet 

plunket 

plymouth 

poil d’ours 

poilu 

poinsettia 

pois green 

polar bear 

polignac 

polo green 

polo tan 

pomegranate 

pomegranate blossom 

pomegranate purple 

pomona green 

pomp and power 

pompadour [green] 

pompeian blue 

pompeian red 

pompeian yellow 

pompeii 

ponce de leon 

ponceau 

pond lily 

pongee 

pontiff [purple] 

pony brown 

popcorn 

popinjay green 

poplar 

poppy [red] 

porcelain 

porcelain blue 

porcelain green 

porraaceous 

porret 

port 

port wine 

portable red 

portugese red or 

portuguese red 

post office red 

posy green 

poudre 

poudre blue 

powder blue 

powder pink 

powdered gold 

prairie 

prairie brown 

praline 

prawn [pink] 

prelate 

primrose green 

primrose yellow 

primuline yellow 

prince grey 

princeton orange 

priscilla 

privet 

prout’s brown 

prune

prune purple 

prunella 

prussian brown 

prussian red 

psyche 

puce 

pueblo 

puke 

pumpkin 

punjab 

puritan grey 

purple aster 

purple brown 

purple heather 

purple lake 

purple madder 

purple navy 

purple ochre 

purple oxide 

purple rubiate 

purrea arabica 

purree 

putty 

pygmalion 

pyrethrum yellow 

pyrite yellow 

pee shell 

pee shell 

quail 

quaker 

quaker blue 

quaker drab 

quaker green 

quaker grey 

queen anne green 

queen blue 

queen’s blue 

queen’s yellow 

quercitron 

quercitron lake 

quimper 

quince yellow 

rabbit 

racquet 

raddle 

radiance 

radiant yellow 

radio 

radio blue 

raffia 

raffia 

ragged sailor 

rail 

rainette green 

raisin 

raisin black 

raisin purple 

rambler rose 

rameau 

rameses 

ramier blue 

rangoon 

raphael 

rapids 

raspberry 

raspberry glace 

raspberry red 

rat 

rattan 

raw italian earth 

raw sienna 

raw umber 

realgar 

red banana 

red bole 

red chalk 

red cross 

red earth 

red in plates 

red lead 

red ochre 

red oxide 

red robin 

redding 

reddle 

redfeather 

redwood 

reed green 

reed yellow 

regal 

regal purple 

regatta 

regimental 

rejane green 

rembrandt 

rembrandt’s madder 

renaissance 

reseda [green] 

resolute 

reveree 

rhododendron 

rhodonite pink 

rhone 

rhubarb 

rifle [green] 

riga 

riga blue 

rinnemann’s green 

ripple green 

risigal 

rivage green 

riviera 

roan 

robin’s egg blue 

robinhood green 

rocou 

rodent 

roe 

roma blue 

roman earth 

roman green 

roman lake 

roman ochre 

roman purple 

roman sepia 

roman umber 

roman violet 

romanesque 

romantic green 

romany 

romarin 

rosalgar 

rosario 

rose amber 

rose ash 

rose beige 

rose blush 

rose breath 

rose caroline 

rose carthame 

rose castor 

rose cendre 

rose d’alma 

rose d’althoea 

rose dawn 

rose de nymphe 

rose de provence 

rose doree 

rose ebony 

rose france 

rose grey 

rose hermosa 

rose hortensia 

rose lake 

rose madder 

rose marie 

rose morn 

rose neyron 

rose nilsson 

rose nude 

rose oak 

rose of picardy 

rose of sharon 

rose petal 

rose pink 

rose quartz 

rose soiree 

rose taupe 

rose-purple 

rosebisque 

rosebloom 

rosebud 

rosedust 

roseglow 

roseleaf 

roselustre 

rosemary 

rosestone 

roset 

rosetan 

rosetta 

rosevale 

rosewood 

roslyn blue 

roucou 

rouge de fer 

rouge vegetal 

royal blue 

royal pink 

royal purple 

royal yellow 

ru ochre 


rubaiyat 

rubber 

rubelite 

ruben’s madder 

rubient 

rubrica 

ruby 

ruby of arsenic 

ruddle 

rufous 

rugby tan 

runnymede 

russet brown 

russet green 

russet orange 

russian blue 

russian calf 

russian green 

russian grey 

russian violet 

rust 

rustic brown 

rustic drab 

rut ochre 

recamier 

sable 

saddle 

safaran 

safflor 

safflower red 

saffor 

saffron yellow 

safrano pink 

sage [green] 

sage drab 

sage grey 

sagebrush green 

sahara 

sailor 

sailor blue 

sakkara 

sallow 

salmon [pink] 

salvia 

salvia blue 

samara 

samovar 

samurai 

sand 

sand dune 

sandalwood 

sandarach 

sandaracha 

sander’s blue 

sanderswood 

sandix 

sandrift 

sandstone 

sandust 

sandy-beige 

sandyx 

sang de boeuf 

sanguine 

sanguineus 

santalwood 

santos 

saona 

sappanwood 

sapphire [blue] 

sappho 

saraband 

saratoga 

saravan 

sarouk 

satinwood 

satsuma 

saturn red 

saturnine red 

saul 

saunder’s blue 

sauterne 

saxe blue 

saxon blue 

saxon green 

saxony blue 

saxony green 

sayal brown 

scabiosa 

scarab 

scarlet 

scarlet in grain 

scarlet lake 

scarlet madder 

scarlet ochre 

scarlet red 

scarlet vermilion

Colour Names 


scheele’s green 

schoenfeld’s purple 

schweinfurt green 

scotch blue 

scotch grey 

sea blue 

sea green 

sea hawk 

sea mist 

sea moss 

sea pink 

sea shell 

sea-water green 

seabird 

seacrest 

seafoam 

seafoam green 

seafoam yellow 

seal [brown] 

seaman blue 

seasan 

seashell pink 

seaside 

seaspray 

seaweed green 

sedge 

seed pearl 

seered green 

seladon green 

seminole 

sepia 

serapi 

serpent 

serpentine 

serpentine green 

service corps 

seville 

seville orange 

sevres [blue] 

shadow 

shadow green 

shagbark 

shale green 

shamrock 

shamrock [green] 

shark 

sheepskin 

sheik 

shell grey 

shirvan 

shrimp [pink] 

shrimp [red] 

siam 

siberian brown 

siberien 

sicilian umber 

siderin yellow 

siena 

sienna brown 

sienna yellow 

siennese drab 

sierra 

signal red 

sil 

silurian gray 

silver fern 

silver green 

silver-wing 

silverpine 

sinbad 

sirocco 

siskin green 

sistine 

skating 

ski 

skimmed-milk white 

skobeloff green 

sky 

sky blue 

sky colour 

sky green 

slag 

slate 

slate [color] 

slate blue 

slate-black 

slate-blue 

slate-green 

slate-grey 

slate-olive 

slate-purple 

slate-violet 

smalt 

smalt green 

smaltino 

smoke blue 

smoke brown 

smoke grey 

smoke yellow 

smoked pearl 

snapdragon 

snowshoe 

soapstone 

solferino 

solitaire 

solitaire 

somalis 

sombrero 

sonora 

soot brown 

sooty black 

sorbier 

sorghum brown 

sorolla brown 

sorrel 

sorrento green 

souci 

source 

spa-tan 

spalte 

spaltum 

spanish brown 

spanish cedar 

spanish flesh 

spanish green 

spanish ochre 

spanish raisin 

spanish red 

spanish wine 

spanish yellow 

spark 

sparrow 

spearmint 

sphinx 

spice 

spinach green 

spinel pink 

spinel red 

sponge 

spray 

spring beauty 

spring green 

springtime 

sprite 

spruce 

spruce ochre 

spruce yellow 

squill blue 

squirrel 

st. benoit 

stag 

starch blue 

stardew 

starflower 

starlight 

starling 

steel 

steel grey 

steeplechase 

stil de grain brown 

stil de grain yellow 

stone blue 

stone grey 

storm grey 

straw [yellow] 

strawberry 

strawberry pink 

striegau earth 

stroller tan 

strontian yellow 

stucco 

succory blue 

sudan 

sudan brown 

suede 

sugar cane 

sulphate green 

sulphine yellow 

sulphur 

sulphur [yellow] 

sultan 

sultana 

sumac 

sunbeam 

sunburn 

sunburst 

sundown 

sunflower [yellow] 

sunglow 

sungod 

sunkiss 

sunlight 

sunni 

sunray 

sunrise yellow 

sunset 

sunstone 

suntan 

superior 

surf green 

surrey green 

swamp 

swedish green 

swedish green 

sweet briar 

sweet pea 

sweet william 

sweetmeat 

swiss blue 

swiss rose 

syrup 

ta-ming 

taffy 

talavera 

tamarach 

tan 

tanagra 

tanaura 

tanbark 

tangerine 

tangier 

tango pink 

tansan 

tapestry 

tapestry red 

tapis vert 

tarragon 

tarragona 

tartan green 

taupe 

tawny 

tawny birch 

tea 

tea green 

tea time 

teak [brown] 

teakwood 

teal blue 

teal duck 

telegraph blue 

tennis 

terra cotta 

terra japonica 

terra lemnia 

terra merita 

terra orellana 

terra orellano 

terra pozzuoli 

terra rosa 

terra siena 

terra sigillata 

terra umber 

terrapin 

terrasse 

testaceous 

thenard’s blue 

thistle 

thistle bloom 

thistletuft 

thrush 

thulite pink 

thyme 

tiber 

tiber green 

tiffin 

tigerlily 

tile blue 

tile red 

tilleul [green] 

tilleul buff 

tinsel 

titania 

titian 

titian gold 

titmouse blue 

toast 

toasted almond 

toboggan 

toga 

tokay 

tokyo 

toltec 

tomato [red] 

tommy red 

topaz 

toquet 

toreador 

torino blue

tortoise 

tyrol 

tortoise shell tyrolese green 

totem 

tyrolian 

tourmaline 

tyrolite green 

tourmaline pink tzarine 

tourterelle 

ultramarine green 

transparent chromium ultramarine yellow 

oxide 

urania blue 

transparent gold ochre vagabond green 

traprock 

valencia 

travertine 

vanda 

trentanel 

vandyke brown 

trianon 

vandyke madder 

triton 

vandyke red 

triumph blue vanilla 

trooper 

vanity 

trotteur tan 

variscite green 

troubador red varley’s grey 

trublu 

vassar rose 

tuileries 

vassar tan 

tulipwood 

vatican 

tunis 

veau d’or 

turbith mineral vegetable red 

turkey blue 

vegetable rouge 

turkey red 

velasquez 

turkey umber velvet brown 

turkish blue 

velvet green 

turkish crescent red venet 

turkish red 

venetian blue 

turmeric 

venetian fuchsia 

turner’s yellow venetian lake 

turquoise [blue] venetian pink 

turquoise green venetian red 

turtle 

venetian rose 

turtle green 

venetian scarlet 

turtledove 

venetian yellow 

tuscan 

venezia 

tuscan brown venice [blue] 

tuscan red 

venice berries 

tuscan tan 

venice green 

tuscany 

venice red 

tussore 

venus 

twilight [blue] verbena 

twine 

verbena [violet] 

twinkle blue verd gay 

tyrian blue 

verdant green 

tyrian pink 

verde vessie 

tyrian violet 

verdet 

verdigris [green] 

verditer green 

verdure 

veridine green 

vernonia purple 

verona brown 

verona yellow 

veronese yellow 

veronica 

versailles 

vert russe 

vervain 

vestal 

veteran 

vetiver green 

victoria 

victoria blue 

victoria green 

victoria lake 

vienna blue 

vienna brown 

vienna green 

vienna lake 

vienna smoke 

vineyard 

viola 

violet-carmine 

violine 

virgin 

viridian 

viridine green 

vitelline yellow 

vitellinous 

vitreous 

wad 

wald 

wall green 

wallflower [brown] 

walnut [brown] 

warbler green 

warm sepia 

water blue 

water cress 

water green 

water grey 

water sprite 

water-color 

waterfall 

waterloo 

watermelon 

watteau 

wau 

wax brown 

wax color 

wax red 

wax white 

wax yellow 

waxen 

weathered oak 

weigelia 

weld 

west point 

westminster 

wheat 

whippet 

whirlpool 

white blue 

white jade 

wield 

wigwam 

wild aster 

wild cherry 

wild dove grey 

wild honey 

wild iris 

wild orchid 

wild pigeon 

wild raspberry 

wild rose 

wild strawberry 

willow 

willow green 

windflower 

windsor 

windsor blue 

windsor green 

windsor tan 

wine dregs 

wine lees 

wine yellow 

wineberry 

winter green 

winter leaf 

wintergreen 

wireless 

wistaria [blue] 


wistaria [violet] 

wistaria blue 

witchwood 

withered leaf 

withered rose 

woad 

woald 

wod 

wode 

wold 

wood rose 

wood violet 

woodash 

woodbark 

woodbine green 

woodland brown 

woodland green 

woodland rose 

wren 

wrought iron 

yacht 

yale blue 

yama 

yellow beige 

yellow berries 

yellow brazil wood 

yellow carmine 

yellow daisy 

yellow earth 

yellow madder 

yellow ochre 

yellow realgar 

yellow sienna 

yellow stone 

yellow wash 

yellow wood 

yellowstone 

yew green 

yolk yellow 

yosemite 

yu chi 

yucatan 

yucca 

yvette violet 

zaffre blue 

zanzibar 

zedoary wash 

zenith [blue] 

zephyr 

zinc 

zinc green 

zinc orange 

zinc yellow 

zinnia 

zulu 

zuni brown 

Reference: Dictionary of Colour, 2001. NBS/ISCC M. [online] Available at: [Accessed 14/04/11].

Colour Names 

Obscure Colour Names and Definitions 

Aeneous 

shining bronze colour 

Albicant 

whitish; becoming white 

Albugineous 

like the white of an eye or an egg; white-coloured 

Amaranthine 

immortal; undying; deep purple-red colour 

Argent 

the heraldic colour silver or white 

Atrous 

jet black 

Aubergine 

eggplant; a dark purple colour 

Aurulent 

gold-coloured 

Azuline 

blue 

Azure 

light or sky blue; the heraldic colour blue 

Badious 

chestnut-coloured 

Beige 

light creamy white-brown 

Brunneous 

dark brown burnet dark brown; dark woollen cloth 

Caesious 

bluish or greyish green 

Cardinal 

deep scarlet red colour 

Castaneous 


Castory 

brown colour; brown dye derived from beaver pelts 

Celadon 

pale green; pale green glazed pottery 

Celeste 

sky blue 

Cerulean 

sky-blue; dark blue; sea-green 

Cesious 

bluish-grey 

Chartreuse 

yellow-green colour 

Chlorochrous 

green-coloured 

Chrysochlorous 

greenish-gold 

Cinerious 

ashen; ash-grey 

Cinnabar 

red crystalline mercuric sulfide pigment; deep red or 

scarlet colour 

Citreous 

lemon-coloured; lemony 

Citrine 

dark greenish-yellow 

Claret 

dark red-purple colour; a dark-red wine 

Coccineous 

bright red columbine of or like a dove; dove-coloured 

Coquelicot 

brilliant red; poppy red 

Corbeau 

blackish green 

Cramoisy 

crimson cretaceous of or resembling chalk; of a whitish 

colour 

Croceate 

saffron-coloured 

Cyaneous 

sky blue 

Eau-de-nil 

pale green colour 

Eburnean 

of or like ivory; ivory-coloured 

Erythraean 

reddish colour 

Ferruginous 

of the colour of rust; impregnated with iron 

Filemot 

dead-leaf colour; dull brown 

Flammeous 

flame-coloured

Flavescent 

yellowish or turning yellow 

Fuliginous 

sooty; dusky; soot-coloured; of or pertaining to soot 

Fulvous 

dull yellow; tawny 

Fuscous 

brown; tawny; dingy 

Gamboge 

reddish-yellow colour 

Glaucous 

sea-green; greyish-blue 

Goldenrod 

dark golden yellow 

Greige 

of a grey-beige colour 

Gridelin 

violet-grey 

Griseous 

pearl-grey or blue-grey; grizzled 

Haematic 

blood-coloured 

Heliotrope 

purplish hue; purplish-flowered plant; ancient sundial; 

signalling mirror 

Hoary 

pale silver-grey colour; grey with age 

Hyacinthine 

of a blue or purple colour 

Ianthine 

violet-coloured 

Ibis 

large stork-like bird; a pale apricot colour 

Icterine 

yellowish or marked with yellow 

Icteritious 

jaundiced; yellow 

Incarnadine 

carnation-coloured; blood-red 

Indigo 

deep blue-violet colour; a blue-violet dye 


Infuscate 

clouded or tinged with brown; obscured; cloudy brown 

colour 

Isabelline 

greyish yellow 

Jacinthe 

orange colour 

Jessamy 

yellow like a jasmine 

Kermes 

brilliant red colour; a red dye derived from insects 

Khaki 

light brown or tan 

Lateritious 

brick-red 

Leucochroic 

white or pale-coloured 

Liard 

grey; dapple-grey 

Lovat 

grey-green; blue-green 

Lurid 

red-yellow; yellow-brown 

Luteolous 

yellowish 

Luteous 

golden-yellow 

Lutescent 

yellowish 

Madder 

red dye made from brazil wood; a reddish or red-orange 

colour 

Magenta 

reddish purple 

Maroon 

brownish crimson 

Mauve 

light bluish purple 

Mazarine 

rich blue or reddish-blue colour 

Melanic 

black; very dark

Colour Names 

Melichrous 

having a honey-like colour 

Meline 

canary-yellow 

Miniaceous 

colour of reddish lead 

Minium 

vermilion; red lead 

Modena 

crimson 

Morel 

dark-coloured horse; blackish colour 

Nacarat 

bright orange-red 

Nankeen 

buff-coloured; durable buff-coloured cotton 

Nigricant 

of a blackish colour 

Nigrine 

black 

Niveous 

snowy; white 

Ochre 

yellowish or yellow-brown colour 

Ochroleucous 

yellowish white 

Olivaceous 

olive-coloured 

Or 

heraldic colour gold or yellow 

Pavonated 

peacock-blue 

Periwinkle 

a bluish or azure colour; a plant with bluish flowers 

Perse 

dark blue or bluish-grey; cloth of such a colour 

Phoeniceous 

bright scarlet-red colour 

Piceous 

like pitch; inflammable; reddish black 

Plumbeous 

leaden; lead-coloured 

Ponceau 

poppy red 

Porphyrous 

purple 

Porraceous 

leek-green 

Prasinous 

leek-green colour primrose pale yellow 

Puccoon 

blood-root; dark red colour 

Puce 

brownish-purple; purplish-pink 

Puniceous 

bright or purplish red 

Purpure 

heraldic colour purple 

Purpureal 

purple 

Pyrrhous 

reddish; ruddy 

Rhodopsin 

visual purple 

Rubiginous 

rusty-coloured 

Rubious 

ruby red; rusty 

Rufous 

reddish or brownish-red 

Russet 

reddish brown 

Sable 

black; dark; of a black colour in heraldry 

Saffron 

orange-yellow 

Sage 

grey-green colour 

Sanguineous 

bloody; of, like or pertaining to blood; blood-red 

Sapphire 

deep pure blue 

Sarcoline 

flesh-coloured

Sepia 

fine brown 

Sinopia 

preparatory drawing for a fresco; reddish-brown colour 

Slate 

dull dark blue-grey 

Smalt 

deep blue 

Smaragdine 

emerald green 

Solferino 

purplish red 

Sorrel 

reddish-brown; light chestnut 

Spadiceous 


Stammel 

coarse woollen fabric, usually dyed red; bright red 

colour 

Stramineous 

strawy; light; worthless; straw-coloured 

Suede 

light beige sulphureous bright yellow 

Tan 

tawny brown 

Taupe 

brownish-grey 

Tawny 

brownish-yellow 

Teal 

greenish-blue 

Terracotta 

reddish-brown 

Testaceous 

of or having a hard shell; brick-red 

Tilleul 

pale yellowish-green 

Titian 

red-gold or reddish-brown 

Topaz 

dark yellow 

Turquoise 

blue-green 


Ultramarine 

deep blue 

Umber 

brownish red 

Vermeil 

bright red or vermilion colour; gilded silver 

Vermilion 

bright red 

Vinaceous 

wine-coloured 

Vinous 

deep red; burgundy 

Violaceous 

violet-coloured violet bluish purple 

Virescent 

becoming green or greenish; of a greenish colour 

Virid 

green 

Viridian 

chrome green 

Vitellary 

bright yellow wallflower yellowish-red 

Watchet 

pale blue 

Wheaten 

the golden colour of ripe wheat 

Whey 

off-white 

Willowish 

of the colour of willow leaves 

Xanthic 

yellow; yellowish 

Zinnober 

chrome green 

Reference: The Phrontistery, nd. Colour terms. [online] Available at: [Accessed 06/04/11].

Colour 

Naming 

Theory

Colour and Language Theory 

Linguistic Relativity and the Color Naming Debate 

Linguistic relativity stems from a question about the 

relationship between language and thought, about 

whether one’s language determines the way one thinks. 

This question has given birth to a wide array of research 

within a variety of different disciplines, especially 

anthropology, cognitive science, linguistics, and 

philosophy. Among the most popular and controversial 

theories in this area of scholarly work is the theory 

of linguistic relativity (also known as the Sapir-Whorf 

hypothesis). An often cited “strong version” of the 

claim, first given by Lenneberg in 1953 proposes that 

the structure of our language in some way determines 

the way we perceive the world. A weaker version of this 

claim posits that language structure influences the world 

view adopted by the speakers of a given language, but 

does not determine it. 

There are two formal sides to the color debate, the 

universalist and the relativist. The universalist side 

claims that our biology is one and the same and so 

the development of color terminology has absolute 

universal constraints, while the relativist side claims 

that the variability of color terms cross-linguistically 

points to more culture-specific phenomena. Because 

color exhibits both biological and linguistic aspects, it 

has become a largely studied domain that addresses 

the linguistic relativity question between language and 

thought. 

The color debate was made popular in large part due 

to Brent Berlin & Paul Kay’s famous 1969 study and 

their subsequent publishing of Basic Color Terms: Their 

Universality and Evolution.[3] Although, most of the 

work on color terminology has been done since Berlin & 

Kay’s famous study, other research predates it, including 

the mid-nineteenth century work of William Ewart 

Gladstone and Lazarus Geiger which also predates 

the Sapir-Whorf hypothesis, as well as the work of Eric 

Lenneberg & Roger Brown in 1950s and 1960s. 

Universalist View 

Berlin and Kay 

The universalist theory that color cognition is an innate, 

physiological process rather than a cultural one was 

started in 1969 by Brent Berlin and Paul Kay in the 

study detailed in their book Basic Color Terms: Their 

Universality and Evolution.[3] The study was intended 

to challenge formerly prevailing theory of linguistic 

relativity set forth by chief linguistic figures Edward Sapir 

and Benjamin Lee Whorf in the Sapir-Whorf Hypothesis. 

They found that there are universal restrictions on the 

number of basic color terms that a language can have 

and the ways in which the language can employ these 

terms. The study included data collected from speakers 

of twenty different languages from a number of different 

language families. Berlin and Kay identified eleven 

possible basic color categories: white, black, red, green, 

yellow, blue, brown, purple, pink, orange, and grey. 

In order to be considered a basic color category, the 

term for the color in each language had to meet certain 

criteria: 

• It is monolexemic. (for example, blue, but not bluish) 

• Its signification is not included in that of any other 

color term. (for example, crimson is a type of red) 

• Its application must not be restricted to a narrow 

class of objects. (for example, blonde is restricted to 

hair, wood) 

• It must be psychologically salient for informants. 

(for example, “the color of grandma’s freezer” is not 

psychologically salient for all speakers) 

In case of doubt, the following “subsidiary criteria” were 

implemented: 

• The doubtful form should have the same 

distributional potential as the previously established 

basic color terms. (for example, you can say reddish 

but not salmonish)

• Color terms that are also the name of an object 

characteristically having that color are suspect, for 

example, gold, silver and ash. 

• Recent foreign loan words may be suspect. 

• In cases where lexemic status is difficult to assess, 

morphological complexity is given some weight as a 

secondary criterion. (for example, red-orange might 

be questionable) 

Berlin and Kay also found that, in languages with 

less than the maximum eleven color categories, the 

colors found in these languages followed a specific 

evolutionary pattern. This pattern is as follows: 

• All languages contain terms for black and white. 

• If a language contains three terms, then it also 

contains a term for red. 

• If a language contains four terms, then it also contains 

a term for either green or yellow (but not both). 

• If a language contains five terms, then it contains 

terms for both green and yellow. 

• If a language contains six terms, then it also contains 

a term for blue. 

• If a language contains seven terms, then it also 

contains a term for brown. 

• If a language contains eight or more terms, then it 

contains a term for purple, pink, orange, and/or grey. 

In addition to following this evolutionary pattern 

absolutely, each of the languages studied also selected 

virtually identical focal hues for each color category 

present. For example, the term for “red” in each of the 

languages corresponded to roughly the same shade in 

the Munsell color system. Consequently, they posited 

that the cognition, or perception, of each color category 

is also universal. 

Additional Universalist Arguments 

A later study supporting this universal, physiological 

theory was done by Kessen, Bornstein, and Weiskopf. 

In this study, sixteen four-month-old infants were 

presented with lights of different frequencies 


corresponding to different colors. The lengths of 

habituation were measured and found to be longer 

when the infant was presented with successive hues 

surrounding a certain focal color than with successive 

focal colors. Kessen, Bornstein and Weiskopf therefore 

claim that the ability to perceive the same distinct focal 

colors is present even in small children. 

Research before Berlin and Kay (1969) 

Gladstone and Geiger 

In their paper Language and thought: Which side are 

you on anyway?, Regier et al. discuss the presence of a 

universalist perspective on the color debate in the mid 

nineteenth century. 

“In the mid-nineteenth century, various scholars, notably 

William Gladstone (1858) and Lazarus Geiger (1880), 

noted that the speakers of ancient written languages did 

not name colors as precisely and consistently – as they 

saw it – as the speakers of modern European languages. 

They proposed a universal evolutionary sequence 

in which color vocabulary evolves in tandem with an 

assumed biological evolution of the color sense”. 

Gladstone was a Homeric scholar and in his writings 

expressed that because there was virtually a lack of 

color terminology in Homeric Greek literature, Greeks 

could probably not see color as we can today. 

“ ... that the organ of color and its impressions were 

but partially developed among the Greeks of the heroic 

age”. 

Geiger expanded on Gladstone’s ideas by looking at 

other classic works and hypothesized that man gradually 

became aware of color over time. He posited the 

idea that this awareness was connected to the order 

colors came up in the spectrum, starting with longest 

wavelengths.


Lenneberg & Roberts 

Lenneberg and Roberts presented their paper The 

Denotata of Color Terms at the Linguistic Society of 

America in 1953. In this paper they reported their 

findings on color recall in Zuni speakers. Zuni has one 

color term for yellow and orange, and Lenneberg and 

Roberts’ study reported that Zuni speakers encountered 

greater difficulty in color recall for these colors than 

English speakers who have available terms to distinguish 

them. Brown and Lenneberg attributed this effect to the 

property of codability. 

Linguistic codability is the ease with which people can 

name things and the effects of naming on cognition and 

behavior. 

Brown & Lenneberg 

Brown & Lenneberg published A Study in Language 

and Cognition in 1954, where they discussed the 

effect of codability on recognition. In their experiment 

they used a series of Munsell chips to test color recall 

and recognition in English speakers. Their findings 

suggested that the availability of a basic color term in 

a given language affected the retention of that color in 

recall testing. Brown & Lenneberg linked their study to 

Lenneberg & Roberts’ 1953 findings on color recall in 

Zuni speakers. 

Relativist View 

Initially, Berlin and Kay’s theory received little direct 

criticism. But in the decades since their 1969 book, a 

significant scholarly debate has developed surrounding 

the universalism of color terminology. Many relativists 

find significant issues with this universalism. Barbara 

Saunders and John A. Lucy are two scholars who are 

prominent advocates of the opposing relativist position. 

Barbara Saunders 

Barbara Saunders believes that Berlin and Kay’s 

theory of basic color terminology contains several 

unspoken assumptions and significant flaws in research 

methodology. Included in these assumptions is an 

ethnocentric bias based on traditions of Western 

scientific and philosophical thought. She regards the 

evolutionary component of Berlin and Kay’s theory 

as “an endorsement of the idea of progress..”. and 

references Smart’s belief that it is “a Eurocentric 

narrative that filters everything through the West and its 

values and exemplifies a universal evolutionary process 

of modernization”. 

With regards to Berlin and Kay’s research, Saunders 

criticizes the translation methods used for the color 

terms they gathered from the 78 languages they had not 

studied directly. Like many others, she also questions 

the effectiveness of using the Munsell color system in 

the elicitation of color terminology and identification of 

focal hues. She feels that “use of this chart exemplifies 

one of the mistakes commonly made by the social 

sciences: that of taking data-sets as defining a 

(laboratory) phenomenon which supposedly represents 

the real world”, and entails “taking a picture of the 

world for the word and then claiming that that picture is 

the concept”. Finally, she takes issue with the anomalous 

cases of color term use that she believes Berlin, Kay and 

Merrifield disregarded in their work on the World Color 

Survey for the purpose of purifying their results. 

In Saunders’ 1997 article with van Brakel, they criticize 

the amount of weight given to study of physiological 

color perception as support for the universalism of color 

terminology. They primarily criticize the idea that there 

is an autonomous neuro-physiological color pathway, 

citing a lack of concrete evidence for its existence. 

Saunders is also bothered by the overall decontextualization 

of color terminology and the failure 

of universalists to address the limitations of their 

methodologies. She points out that: 

“Ordinary colour talk is used in a variety of ways – for 

flat coloured surfaces, surfaces of natural objects, 

patches of paintings, transparent objects, shining 

objects, the sky, flames, illumination, vapours, volumes, 

films and so on, all of which interact with overall 

situation, illumination, edges, textures, patternings and 

distances, making the concept of sameness of colour 

inherently indeterminate”.

John Lucy 

John A. Lucy’s criticisms of Berlin and Kay’s theory 

are similar to those of Saunders and other relativists, 

primarily focusing on shortcomings in research 

methodologies and the assumptions that underlie them. 

Lucy believes that there are problems with how linguistic 

analysis has been used to characterize the meanings of 

color terms across languages. Referential range (what 

a color term can refer to) and grammatical distribution 

(how the term can be used) are two dimensions Lucy 

believes are critical to defining the meaning of a term, 

both of which “are routinely ignored in research on color 

terms which focuses primarily on denotational overlap 

across languages without any consideration of the 

typical use of the terms or their formal status”. He also 

feels that any attempt to contrast color term systems 

requires understanding of each individual language and 

the systems it uses to structure reference. 

Lucy also believes that there is significant bias present 

in the design of Berlin and Kay’s research, due to their 

English-speaking and Western points of view. He thinks 

the use of the Munsell color system demonstrates their 

adherence to the ideas that “speech is about labeling 

accuracy” and that “meaning is really about accurate 

denotation” which he believes “both derive directly 

from the folk understandings of English speakers about 

how their language works”. He refers to Conklin’s study 

of Hanunóo[13] as a demonstration of what a study 

might reveal about a language’s color term system 

when such bias is not present. He demonstrates that “an 

‘adequate knowledge’ of the system would never have 

been produced by restricting the stimuli to color chips 

and the task of labeling”. (original emphasis). 

In summation, he feels that the approach universalists 

have taken in researching color term universals “sets 

up a procedure which guarantees both their discovery 

and their form” and that “it does not really even matter 

whether the researchers involved are open-minded and 

consciously willing to recognize relativism as a possible 


outcome – because the universalist conclusion is 

guaranteed by their methodological assumptions”. 

Recent Scholarship 

Scholarship on color vision has proceeded in three 

principal domains within the last twenty years. There 

have been revisions to the Berlin & Kay hypothesis; in 

response, there have been continued challenges to that 

hypothesis; and lastly, the field of vision science has 

expanded to explore hue categorization at a perceptual 

level, independent of language-based distinctions, 

possibly offering compromise in the two polar theories. 

Revisions of the Berlin & Kay Hypothesis 

In 1999 Paul Kay and Luisa Maffi published an article 

entitled Color Appearance and the Emergence and 

Evolution of Basic Color Lexicons in which they outlined 

a series of revisions in response to data collected in the 

World Color Survey (WCS) and to Stephen Levinson 

and his work on the language Yélî Dnye in Papua New 

Guinea (see below). While upholding an evolutionary 

track for the addition of basic color terms to any given 

lexicon, they outlined a series of three Partition Rules 

(i.e., superordinate rules which determine the evolution 

of BCT’s [mentioned above]): 

1. Black and White (Bk&W): Distinguish black and 

white. 

2. Warm and Cool (Wa&C): Distinguish the warm 

primaries (red and yellow) from the cool primaries 

(green and blue). 

3. Red: Distinguish red. 

The ordering of these rules is reflective of the data of 

the overwhelming majority of languages studied in the 

WCS. However, exceptions do exist, as was accounted 

for by Yélî Dnye and other languages within the WCS. 

Furthermore, they also propose a 0) rule, one which 

simply states: partition. Such a rule is necessary to 

motivate the specification of later basic color terms, 

namely those which can no longer be brought about by 

application of rules 1)-3).


With respect to the evolution of color terms within 

a given lexicon, Kay & Maffi further outlined the 

possibilities of different trajectories of evolution, though 

all of those numerically possible are not attested in the 

World Color Survey. Another significant contribution of 

this article is a discussion of the Emergence Hypothesis 

(see below), its relation to Yélî Dnye, and its motivation 

for the authors’ revision of evolutionary trajectories. 

Opposition to Berlin & Kay et al. 

Here we will overview three approaches to such 

critiques: 1) that brought about by implications within 

the taxonomic structure of the B&K model (as seen 

further in Berlin’s treatment of ethnobiological systems 

of classification); 2) that as seen in research in color 

perception in children and infants; 3) and that brought 

about by specific fieldwork. 

Anna Wierzbicka and Universals of Visual Semantics 

In an article titled The Semantics of Colour: A New 

Paradigm, Wierzbicka discusses three main critiques of 

the Universalist approach: 

The inability to prove the existence of true color terms 

(i.e., those based on variations in hue) in languages 

which lack a superordinate word for color in their 

taxonomies. 

The lack of inquiry into the semantic range of any given 

language’s assumed color naming. 

That the Western Universalist tradition “[imposes] on 

other languages and cultures one’s own conceptual 

grid” and does not reflect “ ‘the native’s point of view’”, 

citing Malinowski in the latter. 

With regard to 1), she states that “the basic point ... is 

that, in many languages, one cannot ask the question, 

‘What color is it?’” The assumption oscillates between 

two versions: on one hand she argues that languages 

which lack a superordinate word for color simply do 

not have minimal color terms; on the other hand she 

argues that even if one contests the first point (i.e., 

agree that languages that lack a word for color still have 

color terms), the fact that one cannot ask the question 

she posits (above) means that color is not a salient 

semantic domain in these languages. In the structure 

of her Natural Semantic Metalanguage, color does not 

constitute a semantic “primitive”, though she argues for 

many others cross-linguistically. (For more on the NSM 

related to color terms, see Theoretical Linguistics 29:3.) 

Pitchford & Mullen: The Developmental Acquisition of 

Basic Colour Terms 

This study compares the evolutionary model of color 

terms of Berlin & Kay to the acquisition of color terms 

in children (something which has been thought to lag 

behind other lexical acquisitions). Their study proceeds 

to three main questions: 

1. Are color terms acquired late? 

2. Are basic color terms acquired in a fixed 

developmental order? 

3. What factors may influence the acquisition of basic 

color terms? 

With regard to 1), they find that color terms are not 

acquired any later than other relevant lexemes to 

distinguish objects. It had been thought, for example, 

that since color is not necessarily unique to a given 

object, and diverse objects are more likely to share 

common color than a common shape, that color terms 

lagged behind shape terms in development. This was 

found not to be the case. 

Second, they found no correlation between the order 

of color term acquisition in children and in languages 

generally. It was found that grey and brown are 

learned later in development; there was no preference 

for the six primary color terms over the remaining 

three secondary ones. The similarity between the 

acquisition of these terms in children and in language 

vocabularies was assumed to be comparable, since

even in current notions of the B&K hypothesis the 

evolutionary order of color terms is thought to be based 

on universals of neurophysiology. While some studies 

in neurophysiology have shown greater salience for 

the basic color terms (and thus correlate their earlier 

evolutionary status), neurophysiology has not been able 

to account for such phenomena as intuitive separations 

of warm and cool colors (the second partition rule 

posited by Kay [see above] is essential to such earlyonset 

warm/cool distinctions, yet is overridden in 

language with a yellow/green/blue color term). 

Levinson & Yélî Dnye 

Yélî Dnye is a language isolate spoken on Rossel Island 

(Yela) in Papua New Guinea. Among observations about 

the class, derivation, usage of and disagreement over 

color naming words in Yélî Dnye is a critique of the 

BCT-model’s assumption that languages which have not 

yet fully lexicalized the semantic space of color (as was 

posited to be universal in the original and subsequent 

B&K papers [1969 &1978]) with the use of all eleven basic 

color names do so by use of the fewer composite terms 

that they do possess (by B&K’s criteria for Yélî Dnye, 

three). As Levinson argues using methodology similar to 

that used by B&K for their initial tests and later for the 

WCS, there are simply regions of the color spectrum 

for which Yélî Dnye has no name, and which are not 

subsumed by larger composite categories, even despite 

the inventive nature of color terms in Yélî Dnye that fall 

outside the criteria for “basic” status. Given the fact 

that such color naming words are extremely inventive, (a 

“semi-productive” mode of adjectival derivation is the 

duplication of related nouns), Levinson argues that this 

is highly detrimental to the BCT-theory, insomuch that 

Yélî Dnye is “a language where a semantic field of color 

has not yet jelled”, and thus one not open to universal 

constraint. 

As Levinson points out, there is evidence that supports 

the emergence of BCT’s through physical objects and 

words used to signify simultaneous properties such 

as lightness. As such, these terms do not cohere as a 


unique, separable semantic domain denoting hue (see 

Bornstein for this criterion). Over time, though, and 

through processes of semantic drift, such a domain can 

emerge. In response to work by Levinson and Lyons, Kay 

dubs this perspective the Emergence Hypothesis (EH). 

(See Levinson’s article for a discussion on the co-existing 

evolutionary tracks for color words if one accepts both 

B&K’s position and the Emergence Hypothesis.) Kay & 

Maffi (1999) incorporate the EH into their evolutionary 

track by removing from their model the assumption 

that languages begin by fully segmenting the color 

spectrum. This inverts their Partition Principles (see 

above), namely by placing 1) and 3) over 0) and 2). 

That is, languages will partially segment the space into 

black, white & red (i.e., 1) & 3)), and then the assignment 

to partition (0)) and split warm and cool colors (2)) 

accommodates the rest of the space. As Kay & Maffi 

explain, this is essential to explications of Y/G/Bu terms 

(e.g., Cree), which were previously incompatible with 

the model. However, this model also introduces the 

possibility for previously divergent evolutionary paths 

for color terms, since it is only after the rearrangement 

and reassignment of the Partition Principles that a 

language that derived from EH origins joins with a 

language that originally partitioned the whole of the 

color spectrum. 

Vision Science and Theoretical Compatibility 

Marc Bornstein’s essay Hue Categorization and Color 

Naming: Physics to Sensation to Perception separates an 

analytical review of vision science and color naming into 

three sections: 

• Categorization: and its aids to both perceptual and 

cognitive functions generally 

• Color Vision and Hue Categorization 

• Color Naming (an unarticulated derivative of the 

first two ideas [see his companion essay Hue 

Categorization and Color Naming: Cognition to 

Language to Culture for a further discussion of this 

point])


As a result, he summarizes both the findings of vision 

science (as it relates to color naming) and the linking 

of three separate but causally related processes within 

the study of color naming phenomena. He states 

that “the physics of color, the psychophysics of color 

discrimination, and the psychology of color naming are 

not isomorphic”. The color spectrum clearly exists at a 

physical level of wavelengths (inter al.), humans crosslinguistically 

tend to react most saliently to the primary 

color terms (a primary motive of Bornstein’s work and 

vision science generally [see Pitchford & Mullen above]) 

as well as select similar exemplars of these primary 

color terms, and lastly comes the process of linguistic 

color naming, which adheres both to universal patterns 

but demonstrates individual uniqueness. While one 

may have origins in its predecessor, variation among 

test subjects in vision science and linguistic variation 

demonstrate that it is not a process of whole causality. In 

his companion essay, he demonstrates that this process 

of causality may indeed be reversed, for the explanation 

of which he employs a set of “models of development”: 

• Undeveloped 

• Partially developed 

• Fully developed 

In response, there are three ways in which outside 

experience may affect this development: through (A) 

induction, (B) modification, or (C) deprivation. Thus the 

logical possibilities are 1A & 1C; 2A, 2B & 2C; and 3B & 

3C. Using this format, he explains that developmental 

altering in hue categories “entail perceptual 

‘sharpening’ and ‘broadening’”. He attributes this to 

either “maturation” (perceptually) or “experience”. 

Such a conclusion is necessarily indeterminate because 

understanding of why certain hue categories are lost 

and others induced (c.f. developmental processes 

above) “requires further exacting research”. Coming 

from these two perspectives (i.e., those outlined in the 

causation above, and the models of development), this 

leads Bornstein to conclude that “there appear to be 

nontrivial biological constraints on color categorization 

[and that] ... the available evidence seems compatible 

with a position of moderate universality that leads to 

expectations of probabilistic rather than deterministic 

cross-cultural correspondence”, and that “in color, 

relativism appears to overlay a universalist foundation”. 

Reference: Wikipedia, 2010. Linguistic relativity and the colour naming debate. [online] Available at: 


Umberto Eco Would Have Made a Bad Fauve by Mark Mussari 

“The eye altering, alters all.” 

- Blake 

In his essay “How Culture Conditions the Colours We 

See,” Umberto Eco claims that chromatic perception 

is determined by language. Regarding language as 

the primary modeling system, Eco argues for linguistic 

predominance over visual experience: “... the puzzle 

we are faced with is neither a psychological one nor 

an aesthetic one: it is a cultural one, and as such is 

filtered through a linguistic system” (159). Eco goes on 

to explain that he is ‘very confused’ about chromatic 

effect, and his arguments do a fine job of illustrating 

that confusion. To Eco’s claim that color perception is 

determined by language, one can readily point out that 

both babies and animals, sans language, experience-and 

respond to--color perception. How then can color 

be only a cultural matter? 

Eco attempts to make a connection between the 

“negative concept” of a geopolitical unit (e.g., Holland 

or Italy defined by what is not Holland or Italy) and a 

chromatic system in which “units are defined not in 

themselves but in terms of opposition and position in 

relation to other units” (171). Culture, however, is not the 

only determinant in the opposition that defines certain 

colors: It is a physiological phenomenon that the eye, 

after staring at one color (for example, red) for a long 

time, will see that color’s complement, its opposite 

(green), on a white background. 

Language is a frustrating tool when discussing color: 

languages throughout the world have only a limited 

number of words for the myriad color-sensations 

experienced by the average eye. Though language 

training and tradition have an undoubtedly profound 

effect on our color sense, our words for color constitute 

only one part of the color expression and not always the 

most important one. In his Remarks on Colour (1950-51), 

Wittgenstein observed: ‘When we’re asked ‘What do 

the words ‘red’, ‘blue’, ‘black’, ‘white’ mean?’ we can, of 

course, immediately point to things which have these 


colours,--but our ability to explain the meanings of 

these words goes no further!’ (I-68). We can never say 

with complete certainly that what this writer meant by 

this color (we are already in trouble) is understood by 

this reader (the woods are now officially burning). 

A brief foray into the world of color perception discloses 

that, first and foremost, a physiological process, not a 

cultural one, takes place when a person sees colors. In 

his lively Art & Physics (1991), Leonard Shlain observes 

that “Color is the subjective perception in our brains 

of an objective feature of light’s specific wavelengths. 

Each aspect is inseparable from the other” (170). In his 

1898 play To Damascus I, August Strindberg indicated 

specifically in a stage direction that the Mourners and 

Pallbearers were to be dressed in brown, while allowing 

the characters to defy what the audience saw and claim 

that they were wearing black. In what may well be the 

first instance of such dramatic toying with an audience’s 

perception, Strindberg forces us to ask where colors 

exist: In the subject’s eye or in the perceived object? 

In no other feature of the world does such an interplay 

exist between subject and object. Shlain notes that 

color “is both a subjective opinion and an objective 

feature of the world and is both an energy and an 

entity” (171). In the science of imaging (the transfer of 

one color digital image from one technology to another) 

recent research has suggested that human vision may be 

the best model for this process. Human vision is spatial: 

it views colors also as sensations involving relationships 

within an entire image. This phenomenon is part of the 

process of seeing and unique to the way humans see. 

In some ways color terms illustrate Roland Barthes’s 

arguments (in S/Z) that connotation actually precedes 

denotation in language--possibly even produces what 

we normally consider a word’s denotation. Barthes 

refers to denotation as ‘the last of connotations’ (9). 

Look up ‘red’ in the American Heritage Dictionary and 

the first definition you find is a comparison to ‘blood.’ 

Blood carries with it (or the reader brings to it) a number

Colour Naming Theory 

of connotations that have long inspired a tradition of 

associating red with life, sex, energy, etc. Perhaps the 

closest objective denotation for red is the mention 

of ‘the long wavelength end of the spectrum,’ which 

basically tells us nothing about experiencing the color 

red. Instead, the connotations of red, many of them 

based on previous perceptual experience, constitute 

our first encounter with the word ‘red.’ I would not be so 

inclined to apply Barthes’s connotational hierarchy when 

one sees red in, say, a painting--an experience in which 

some of the subjectivity one brings to a color is more 

limited by the actual physical appearance of the hue 

chosen by the artist. 

Also, though Barthes talks about linguistic associations, 

colors are more inclined to inspire emotional 

associations which sometimes cannot be expressed 

in language. As Gaston Bachelard wrote in Air and 

Dreams: An Essay on the Imagination of Movement: 

‘The word blue designates, but it does not render’ (162). 

Still, the ‘pluralism’ Barthes argues for in reading seems 

particularly present in the reader’s encounter with color 

terms and their constant play of objectivity/subjectivity. 

In painting color was first released from the confines of 

form by the Post-Impressionists Cézanne, Gauguin, and 

van Gogh, who allowed the color of the paint, the very 

marks on the canvas, to carry the power of expression. 

Following their lead, the French Fauve painters, under 

the auspices of Matisse, took the power of color 

another step further. Perhaps the greatest colorist of 

the twentieth century, Matisse understood that colors 

possess a harmony all their own--that colors call out for 

their complements; he used this knowledge to paint 

some of the most harmonious canvases in the history 

of art. ‘I use the simplest colors,’ Matisse wrote in ‘The 

Path of Color’ (1947). ‘I don’t transform them myself, it 

is the relationships that take care of that’ (178). When 

he painted the Red Studio, for example, the real walls 

were actually a blue-gray; he later said that he ‘felt 

red’ in the room--and so he painted red (what he felt), 

leaving the observer to see red (what she feels). Other 

than its descriptive function, what does language have 

to do with any of this? It is a matter of perception and 

emotion. 

At a 1998 Seattle art gallery exhibit of predominantly 

monochromatic sculptures featuring icy white glass 

objects, I asked the artist why he had employed so 

little color in his work (there were two small pieces 

in colored glass and they were not as successful). He 

replied that “color has a tendency to get away from 

you,” and so he had avoided it as much as possible. The 

fact that color has a power all its own, that the effects of 

chromaticism depend partially on how colors function 

beyond the associations applied to them, has long been 

acknowledged by more expressionistic artists. Writing 

to Emile Bernard in 1888, van Gogh proclaimed: ‘I 

couldn’t care less what the colors are in reality.’ 

The pieces of the color puzzle which Umberto Eco 

wishes to dismiss, the psychological and the aesthetic, 

actually serve as the thrust of most pictorial and literary 

uses of color spaces. Toward the end of his essay, Eco 

bows to Klee, Mondrian, and Kandinsky (including 

even the poetry of Virgil) and their “artistic activity,” 

which he views as working “against social codes and 

collective categorization” (175). Perhaps these artists 

and writers retrieved color from the deadening and 

sometimes restrictive effects of culture. Committed to 

the notion that the main function of color is expression, 

Matisse liberated color to abolish the sense of distance 

between the observer and the painting. His innovations 

are still baffling theorists: In Reconfiguring Modernism: 

Exploring the Relationship between Modern Art and 

Modern Literature, Daniel R. Schwarz bemoans the 

difficulty in viewing Matisse’s decorative productions in 

‘hermeneutical patterns’ (149). Like Eco, Schwarz wants 

to replace perception and emotion with language and 

narrativity. 

Language may determine how we express the 

experience of color, but Eco places the cart before the 

horse if he actually believes that language ‘determines’

chromatic experience. Eco is not alone: the Cambridge 

linguist John Lyons, observing that color is ‘not 

grammaticalised across the languages of the world 

as fully or centrally as shape, size, space, time’ (223), 

concludes that colors are the product of language under 

the influence of culture. One is reminded of Goethe’s 

remark that “the ox becomes furious if a red cloth is 

shown to him; but the philosopher, who speaks of color 

only in a general way, begins to rave” (xli). 

References 

Bachelard, Gaston. Air and Dreams: An Essay on the 

Imagination of Movement. Dallas: The Dallas Institute 

Publications, 1988. 

Barthes, Roland. S/Z. Trans. Richard Miller. New York: Hill 

and Wang, 1974. 

Eco, Umberto. ‘How Culture Conditions the Colours 

We See.’ On Signs. Ed. M. Blonsky. Baltimore: Johns 

Hopkins University Press, 1985. 157-75. 

Goethe, Johann Wolfgang. The Theory of Colors. Trans. 

Charles Lock Eastlake. Cambridge: The MIT Press, 1970. 

Lyons, John. ‘Colour in Language.’ Colour: Art & 

Science. Ed. Trevor Lamb and Janine Bourriau. 

Cambridge: Cambridge University Press, 1995. 194-224. 

Matisse, Henri. Matisse on Art. Ed. Jack Flam. Rev. ed. 

Berkeley: University of California, 1995. 

Riley, Charles A., II. Color Codes: Modern Theories of 

Color in Philosophy, Painting and Architecture, Literature, 

Music and Psychology. Hanover: University Press of New 

England, 1995. 

Schwarz, Daniel R. Reconfiguring Modernism: 

Explorations in the Relationship between Modern Art 

and Modern Literature. New York: St. Martin’s, 1997. 


Shlain, Leonard. Art & Physics: Parallel Visions in Space, 

Time & Light. New York: Morrow, 1991. 

Strindberg, August. To Damascus in Selected Plays. 

Volume 2: The Post-Inferno Period. Trans. Evert 

Sprinchorn. Minneapolis: University of Minnesota Press, 

1986. 381-480. 

Van Gogh, Vincent. The Letters of Vincent van Gogh. 

Trans. Arnold Pomerans. London: Penguin, 1996. 

Reference: MUSSARI, M., 2002. Umberto Eco would have made a bad fauve. Media & Culture Journal, [online] Available at: [Accessed 20/08/10].


The Effects of Color Names on Color Concepts, or Like Lazarus Raised from the Tomb 

After I finally finished Language in Mind, about which 

I posted the other day, I went back and looked at 

some of the literature on linguistic relativity that I had 

read over the years, but had mostly forgotten. And 

since linguistic relativity has always been a favorite 

topic of mine, I thought I’d post a little more about it 

(it may not be a favorite topic of yours, but hey, this is 

my blog!). In the early days of cognitive science, the 

majority of the studies designed to test the Sapir-Whorf 

hypothesis, or other linguistic relativity hypotheses, 

looked at the effects of color terms on color concepts. 

Early on, much of this work produced promising results 

for supporters of the S-W hypothesis. But in 1972, E.R. 

Heider published a paper titled “Universals in color 

naming and memory” that effectively killed the Sapir- 

Whorf hypothesis 1 . Heider compared memory for 

colors in speakers of two different languages, English 

and the language of the Dugum Dani. The Dugum 

Dani is a remote hunter-gatherer tribe in New Guinea 

that has had little exposure to western culture. They 

have two basic color terms, compared to 11 in English. 

Thus, the comparison between the two presented a 

particularly strong test of the Sapir-Whorf hypothesis: 

if the speakers of a language with 2 color terms and 

the speakers of a language with 11 both have the same 

or highly similar color concepts, then we’re justified 

in dropping the idea of linguistic relativity, at least for 

color. And that’s what Heider found: English speakers 

and members of the Dugum Dani tribe displayed highly 

similar color memory. Other researchers found similar 

evidence in the comparisons of speakers of several other 

languages with varying numbers of color terms, but for 

all intents and purposes, linguistic relativity was dead 

after Heider. 

Until the 1990s, that is. Looking at color terms presents a 

test of a particularly strong version of linguistic relativity. 

Color is a concrete, physically-defined category with 

a well-known neural basis. Thus, the explanation for 

Heider’s data, which was widely accepted, was that 

color concepts are determined not by color names, but 

by the physiology of color perception. However, in the 

90s, researchers began to think color was too strong a 

test, and that for more abstract domains, language does 

play a constraining role. Over the last several years, a 

growing body of evidence has shown that this is in fact 

the case. For abstract domains like time, number, space, 

and substance, language can be highly influential. But 

anyone who’s really interested in linguistic relativity 

always has color in the back of his or her mind. If 

evidence for universal color categories can kill the Sapir- 

Whorf hypothesis, then evidence for cultural variance in 

color categories can bring it back to life. 

But there are difficulties in studying cultural variation 

in color. You can’t just study the speakers of languages 

spoken by people in industrialized nations, because 

there has been a lot of interaction between the speakers 

of those languages. You can’t even study languages 

spoken exclusively by people in unindustrialized 

nations who have had a lot of exposure to western 

culture, because speakers of those languages tend to 

adopt color terms from western languages (especially 

from English). So, you have to find remote tribes that 

speak languages that have had relatively little outside 

influence. That takes money and time. Furthermore, 

there is always the problem of running the same 

experiment in multiple languages. You never know 

whether the experiment is exactly the same to speakers 

of different languages (and if you’re an adherent of 

some version of linguistic relativity, you have to believe 

that it isn’t!). That’s a particularly big problem when 

you’re studying members of remote hunter-gatherer 

tribes. Psychology experiments seem weird to American 

undergraduates who are taking a course about 

psychology and its experiments. Imagine how odd they 

must seem to hunter-gatherers who’ve never heard of 

psychology. But Heider’s experiments suffered from 

these problems, too, so if there’s reason to doubt any 

cross-cultural research on color concepts, then there’s 

reason to doubt Heider’s. For some, that doubt is all the 

motivation they need to do more research. 

Enter Debi Roberson, and her colleagues. Roberson 

believes that there is evidence in Heider’s data that 

Heider’s conclusions may have been a bit hasty. For

instance, Dani color memory was much worse than that 

of English speakers, even though the error patterns 

were highly similar. Heider has no explanation for this. 

Perhaps it is an indication that Dani color concepts really 

are different from those of English speakers. Armed 

with her doubt of Heider’s data, Roberson set out to 

replicate his results, and further test the effects of color 

names on color concepts using new methods. For this 

post, I’ll describe two of her methods: color memory, 

which attempts to replicate Heider’s findings, and 

categorical perception. 

The experiments on color memory go like this. First, 

you have to determine the number of basic color terms 

in a language. You do this by having people name 

Munsell color chips, which depict colors across the 

visible spectrum. You then determine the color names 

that were used to describe the bulk of the spectrum. In 

doing so, you get graphs that look like the following for 

English-speakers (from Roberson et al. 2000 2 : 

The numbers at the top and on the side (which are 

hard to see, I know) are the numbers and labels for the 

Munsell chips. While you’re eliciting color names, you 

also ask the participants to indicate the best example of 

each color (the most common answer to this question 

for each name is represented in the above graph by 

the dots). Roberson and her colleagues have done this 

for three cultures, English (in the graph above, which 

gives 10 basic color terms, as compared to the 11 that 

Heider found), as well as the Berinmo tribe, which is also 


from New Guinea, and the Himba tribe from Namibia. 

Both the Berinmo and Himba tribes have had very little 

exposure to western culture, and their languages lack 

any color terms borrowed from other languages. Both of 

them have five color terms. Here are the graphs of their 

color names, which you can compare to the graph of 

English color names above 3 : 

The Berinmo and Himba graphs look somewhat similar, 

but are different enough for comparison. The numbers 

on these two graphs indicate the number of participants 

who said that that chip was the best example. 

After you’ve got the naming data, you can do the 

memory task. The materials for the task include low 

saturation color chips (i.e., chips that are near naming 

boundaries, or otherwise far from the best examples 

of a color name) from the English color categories. The 

participants are shown a chip by itself for five seconds, 

and then the chip is covered. After thirty seconds, the


participants are shown a full array of chips (40 total) and 

asked to identify the color they had just seen. The low 

saturation chips are chosen because they will produce 

high error rates. The key data is what sorts of errors 

participants make. If they tend to mistakenly choose 

other chips from the same color category in English, 

as English participants do, then we can infer that color 

categories are universal. However, if there errors tend 

to involve choosing chips that have the same name 

in Berinmo or Himba, then we can reason that color 

terms affect color perception and memory, and thus 

that color categorization is not universal. This would be 

strong evidence for linguistic relativity in color concepts. 

And that’s what Roberson and her colleagues found. 

Berinmo participants tended to make errors consistent 

with their color names, while Himba participants made 

errors consistent with theirs. Neither made errors 

consistent with English color names (the correlations 

between naming and memory for Himba participants 

were r = .559 for memory and Himba names and r = 

.036 memory and English names; the correlations were 

similar for Berinmo vs. English for Berinmo speakers). 

To provide further evidence that color names affect 

color categories, Roberson et al. (2000) and Roberson 

et al. (2005) conducted an experiment on categorical 

perception with the Berinmo and Himba. In categorical 

perception, within-category exemplars tend to be 

treated as more similar than between-category 

exemplars, even when the between-category exemplars 

are more similar physically. This is particularly interesting 

in color perception: a color exemplar classified as 

red will be more similar to other exemplars of red, 

particularly the best examples of red, than it will be to 

examplars of neighboring colors, even if the exemplar 

falls on the physical boundary between the two colors 

and is thus closer, physically, to exemplars from the 

neighboring colors than it is to the best example of red. 

If Berinmo and Himba speakers demonstrate categorical 

perception effects consistent with their labels, but not 

with other labels (particularly English), then we can 

conclude that their color naming affects their color 

concepts. 

To test this with Berinmo speakers, they tested 

participants on a category distinction present in 

English, but not Berinmo (green-blue) and one present 

in Berinmo, but not English (“nor” and “wor,” as in the 

graph above). They presented participants with three 

Munsell color chips, and asked them which two were the 

most similar to each other. Two of the chips were highly 

physically similar, i.e., they were close to each other in 

the physical color space. One of those two chips also 

shared the same label as the third chip. Thus, you might 

have two “wor” chips, and one “nor” chip, with the 

“nor” chip being physically more similar to one of the 

“nor” chips than the other “nor” chip. If participants 

consistently answer that the two “nor” chips are more 

similar than the physically similar “nor” and “wor” chips, 

then they will have exhibited a categorical perception 

effect. Furthermore, if they do not exhibit a categorical 

perception effect for the green-blue distinction (i.e., 

they pick the more physically similar chips when the 

choices are two classified as green and one as blue), 

then we can conclude that it is the naming, rather than 

any universal physiological aspect of color perception, 

that is driving the categorical perception effect. And 

that’s what Roberson et al. (2000) found for Berinmo. 

Roberson et al. (2005) found similar categorical 

perception effects for the Himba speakers. 

It’s interesting that the Berinmo and Himba tribes 

have the same number of color terms, as well, because 

that rules out one possible alternative explanation of 

their data. It could be that as languages develop, they 

develop a more sophisticated color vocabulary, which 

eventually approximates the color categories that are 

actually innately present in our visual systems. We would 

expect, then, that two languages that are at similar 

levels of development (in other words, they both have 

the same number of color categories) would exhibit 

similar effects, but the speakers’ of the two languages 

remembered and perceived the colors differently. Thus 

it appears that languages do not develop towards any 

single set of universal color categories. In fact, Roberson 

et al. (2004) reported a longitudinal study that implies

that exactly the opposite may be the case 4 . They found 

that children in the Himba tribe, and English-speaking 

children in the U.S., initially categorized color chips 

in a similar way, but as they grew older and more 

familiar with the color terms of their languages, their 

categorizations diverged, and became more consistent 

with their color names. This is particularly strong 

evidence that color names affect color concepts. 

It appears, then, that Roberson and her colleagues have 

laid their hands on the Sapir-Whorf hypothesis and 

raised it from the dead with their experiments using 

members of the Berinmo and Himba tribes. We should, 

of course, take these results with a healthy dose of 

skepticism, because it does involve testing people in 

very different languages and cultures and comparing 

their results, which, as I said earlier, is a big problem. 

However, the growing body of evidence from Roberson 

and her colleagues’ experiments is hard to deny. I don’t 

know about you folks, but I find the revival of Sapir- 

Whorf incredibly exciting. 

References 

1 Heider, E.R. (1972). Universals in color naming and 

memory. Journal of Experimental Psychology, 93, 10-20. 

2 Roberson, D., Davies I. & Davidoff, J. (2000) Colour 

categories are not universal: Replications and new 

evidence from a Stone-age culture. Journal of 

Experimental Psychology: General , 129, 369-398. 

3 The Himba graph is from Roberson, D., Davidoff, 

J., Davies, I. & Shapiro, L. (2005) Colour categories in 

Himba: Evidence for the cultural relativity hypothesis. 

Cognitive Psychology, 50, 378-411. 

4 Roberson, D., Davidoff, J., Davies, I.R.L. & Shapiro, L. 

R. (2004) The Development of Color Categories in Two 

languages: a longitudinal study. Journal of Experimental 

Psychology: General, 133, 554-571. 


Reference: CHRIS, 2005. The Effects of Color Names on Color Concepts, or Like Lazarus Raised from the Tomb. Mixing Memory, [blog] 14 August, Available at: 



Color Vocabulary and Pre-attentive Color Perception by Paul Kay 

Do the well-demonstrated Whorfian effects in color 

discrimination really reach down to the level of 

perception? Some recent research suggests that 

Whorfian effects may exist at a level that is literally 

perceptual. 

The term “Categorical Perception” (CP) names 

people’s propensity to make finer discriminations 

at the boundaries between categories than at their 

interiors. It is well established that differences between 

languages in the boundaries of color terms induce 

corresponding differences in categorical perception in 

their speakers. For example, if a language A , like Greek 

or Russian, makes a simple lexical distinction between 

light blue and dark blue and language B, like English 

or Japanese, does not, speakers of A will reliably make 

finer discriminations at the boundary between light 

and dark blue than at the interiors of these categories 

and speakers of language B will not do this. There has 

been quite a bit of research on this question, some of 

it discussed in this forum: here, here, and here; all this 

research points to the conclusion that color naming 

differences across languages induce predictable 

differences in categorical perception of color. 

A debated issue, however, concerns whether the word 

“perception” in the expression “categorical perception” 

is being used with sufficient precision. Are the 

observed effects truly perceptual or do they reflect a 

level of psychological — i.e., neural — processing that is 

either response-related or otherwise post-perceptual? 

Specialists in visual psychophysics and physiology 

(areas I am not expert in) are careful not to use the word 

“perception” loosely. However, consensus on a bright 

line criterion separating perception senso strictu from 

everything that take place in our brains downstream 

from perception does not, so far as I can tell, yet exist. 

Some interesting new research both proposes a 

reasonable criterion for marking off perception per se 

from post-perceptual processing and demonstrates 

that language-induced (i.e., Whorfian) color CP is, 

by this strict criterion, perceptual. According to 

Thierry, Athanasopulous, Wiggett, Dering, & Kuipers, 

“Unconscious effects of language-specific terminology 

on preattentive color perception”, PNAS (in press, 2009), 

neural activity is perceptual if it’s both unconscious and 

pre-attentive. (I think some earlier color CP research 

can be argued to also meet this criterion, but that’s a 

judgment call and this is a side issue, which doesn’t 

detract in any way from the interest of the new stuff.) 

[Note by Mark Liberman:The link is not yet live, although 

the paper was released from embargo on 2/16/2009, 

according to a note from editorial staff at PNAS; so until 

PNAS gets around to putting the paper on their site, a 

copy is here. A delay of up to ten days between the end 

of the embargo and “Early Edition” access is a typical 

pattern for PNAS, though the reason is unclear.] 

Thierry et al. use an event-related potential (ERP) 

procedure, in which the subject is hooked up to an 

EEG recording device and the reaction of the brain’s 

electrical activity is monitored while he or she is 

exposed to a series of stimuli. The series consists 

of repetitions of a “standard” stimulus, occasionally 

interrupted by a different, “deviant” stimulus. The 

brain has a characteristic way of reacting to a novel 

stimulus (of any kind), and according to Thierry et al. this 

reaction pattern is both unconscious and pre-attentive: 

unconscious because subjects are unaware of it and 

pre-attentive because in such experiments the subjects 

are instructed to react to some aspect of the stimulus 

unrelated to the property being monitored and the 

brain activity of the trials which elicit a response are not 

included in the analysis. For example, in this experiment 

the subjects were shown mostly colored circles with 

an occasional colored square and instructed to react 

when the stimulus was square. However, the brain wave 

patterns were analyzed only for constancy or deviance 

in the color of the circles, not the shape of the stimulus, 

which was the focus of attention. That part is crucial.

Subjects were speakers of either English, which makes 

no basic color term distinction between light and 

dark blue, or of Greek, which does: ghalazio and ble, 

respectively. Subjects of both groups were tested in 

four blocks of trials. The researchers “instructed the 

participants to press a button when and only when they 

saw a square shape (target, probability 20%) within 

a regularly paced stream of circles [of the same hue] 

(probability 80%). Within one block the most frequent 

stimulus was a light or dark circle (standard, probability 

70%) and the remaining stimuli were circles [of the same 

hue] with a contrasting luminance (deviant, probability 

10%), i.e., dark if the standard was light or vice versa.” 

That is, there were trials of the types , , , and , as shown in Figure 1 from their paper: 

Trial type was cross-categorized with speaker type into 

four groups of trials, Greek-blue, English-blue, Greekgreen, 

English-green. The standard novelty reaction 

(“visual mis-match negativity” or vMMN) occurs in the 

neighborhood of 200 milliseconds. This decrease in 

electrical potential was found to be significantly greater 

in the Greek-blue set of trials than in any of the other 

three sets, which were among themselves statistically 

indistinguishable, as shown in their Figure 2: 


Unfortunately, the interpretation of the figure doesn’t 

immediately leap out at the viewer, so here’s part of the 

authors’ statistical explanation: 

Cricitally, we found no overall main effect of color (P 

> 0.1) or participant group (P > 0.1) and no significant 

color by group interaction on the mean amplitude of the 

vMMN but, as predicted, a significant, triple interaction 

between participant group, color, and deviancy (F[1, 38= 

= 4.8, P < 0.05; Fig. 2B). Post hoc tests confirmed that 

this interaction was generated by a differential vMMN 

response pattern in Greek and English participants, 

such that the vMMN effect was numerically (but not 

significantly greater for green than blue deviants in the 

English participants (F[1,38]=0.9, P > 0.1) but significantly 

greater for blue than green deviants in Greek 

participants (F[1, 38] = 7.1, P < 0.02), whereas the vMMN


effect for green deviants was of similar magnitude in 

both the participant groups (F[1, 38] = 0.27, P > 0.1). 

In less technical terms: Language differences in color 

categories were associated with differences between 

their speakers in perception in the strict sense of the 

word (well, at least in a strict sense of the word). The 

differences (in reactions to differences between withincategory 

and across-category colors) were not large 

ones, but they were statisticially significant. And they 

were apparently too early to be the result of assigning 

and comparing color names on a given trial: they must 

have been the result of biases introduced into the color 

perception system itself. 

So, what does this finding mean in the grand scheme 

of things? I think, if replicated, it suggests strongly that 

language difference can in fact influence perception. 

But now the question turns to the relative importance 

of that influence. The broadest Whorfian view espoused 

by serious contemporary experimentalists, such as Jules 

Davidoff, Debi Roberson, and (sometimes) Ian Davies, 

holds (or held until recently) that color terminologies 

can vary arbitrarily across languages, constrained only 

by the rule that a named category cannot occupy 

discontinuous regions of color space. (See, for example, 

Roberson, Davies & Davidoff 2000. Actually, their 

language is a bit vague on this, but here is not the place 

to sort out fine points of scientific rhetoric.) 

However, several independent analyses of the World 

Color Survey data have shown beyond any reasonable 

doubt that this is not the case: there are universal 

tendencies in color naming across all languages (See, 

for example, Kay & Regier 2003, Regier, Kay & Cook 

2005; Lindsey & Brown 2006; Griffin 2006; Kuehni 

2007; Webster & Kay 2007; Dowman 2007). So 

languages may differ a lot in how they name colors, 

but these differences are constrained by a seeming 

master plan, which may have to do with our common 

internal representation of color space (Jameson & 

D’Andrade 1997, Regier, Kay & Khertepal 2007). A 

second constraint on wild Whorfianism is this: the 

color CP effect in speaking adults has been shown to 

be restricted (probably entirely, but certainly in major 

degree) to the right visual field, which projects to the 

left (language dominant) brain hemisphere (Gilbert 

et al. 2006, Drivonikou et al. 2007, Roberson, Pak, & 

Hanley 2008). This suggests that at every moment 

of perception, half of our visual input is filtered by 

language, but not the other half. Concurrent verbal 

interference (e.g., mentally rehearsing a long number 

name) has been shown in both lateralized and nonlateralized 

color CP studies to suppress the CP effect, 

indicating that neural representations of the named 

color categories are activated online during these 

experiments. 

So it looks as if language may indeed influence color 

perception in a strict sense, but if that is true — and 

these results show surprisingly that it seems to be true 

— it does this in a way that is constrained by the facts 

that (1) there are cross-language commonalites in the 

internal representation of color and (2) that only half of 

our visual inputs are language-filtered. 

References 

Cook, R. S., P. Kay, and T. Regier (2005). “The World 

Color Survey database: History and use”. In Henri 

Cohen and Claire Lefebvre (Eds.), Handbook of 

Categorization in Cognitive Science. Elsevier. 

Davidoff, J., Davies, I. & Roberson, D. (1999). “Colour 

categories of a stone-age tribe”. Nature 398, 203-204. 

Drivonikou G.V., P. Kay, T. Regier, R. B. Ivry, A. L. Gilbert, 

A. Franklin, & I. R. L. Davies (2007). “Further evidence 

that Whorfian effects are stronger in the right visual field 

than the left”. Proceedings of the National Academy of 

Sciences, 104, 1097-1102. 

Franklin, A., Drivonikou, G.V., Bevis, L., Davies I. R. L., 

Kay, P. & Regier, T. (2008a). “Categorical perception of 

color is lateralized to the right hemisphere in infants,

ut to the left hemisphere in adults”. Proceedings of the 

National Academy of Sciences, 105, 3221-3225. 

Gilbert, A. L., Regier, T., Kay. P. & Ivry R. B. (2006). 

“Whorf hypothesis is supported in the right visual field 

but not the left”. Proceedings of the National Academy 

of Sciences 103, 489-494. 

Gilbert, A. L., Regier, T., Kay. P & Ivry, R. B. (2008). 

“Support for lateralization of the Whorf effect beyond 

the realm of color discrimination”. Brain and Language 

105, 91-98. 

Kay, P. & Regier, T. (2003). “Resolving the question of 

color naming universals”. Proceedings of the National 

Academy of Sciences, 100, 9085-9089. 

Kuehni, Rolf G. (2007). “Nature and culture: An analysis 

of individual focal color choices in World Color Survey 

languages”. Journal of Cognition and Culture 7, 151-172. 

Lindsey, Delwin T. & Angela G. Brown (2006). 

“Universality of color names”. Proceedings of the 

National Academy of Sciences, 103, 16609-16613. 

Regier, T., P. Kay, and R. S. Cook (2005). “Focal colors 

are universal after all”. Proceedings of the National 

Academy of Sciences 102:8386-8391. 

Regier, T., Kay, P. & Khetarpal, N. (2007). “Color naming 

reflects optimal partitions of color space”. Proceedings 

of the National Academy of Sciences, 104, 1436-1441. 

Roberson, D., Davies I. & Davidoff, J. (2000). “Colour 

categories are not universal: Replications and new 

evidence from a Stone-age culture”. Journal of 

Experimental Psychology: General, 129, 369-398 

Roberson, D., Pak, H. J. & Hanley, R. (2008). “Categorical 

perception of colour in the left and right visual field is 

verbally mediated: Evidence from Korean”. Cognition 

107, 752-762. 


Thierry, D., Athanasopulous, P., Wiggett, A., Dering, B., 

& Kuipers, J-R (2009) “Unconscious effects of languagespecific 

terminology on pre-attentive color perception”. 

PNAS (as of 2/22/2009, in limbo between embargo and 

Early Edition). 

Webster, Michael A. & Kay. P. (2007). “Individual and 

population differences in focal colors”. In: MacLaury, 

Robert E., Galina V. Paramei and Don Dedrick (eds.), 

Anthropology of Color: Interdisciplinary multilevel 

modeling. pp. 29Ð53. 

Reference: KAY, P., 2009. Colour vocabulary and pre-attentive colour perception. Language Log blog, [blog] 23 February, Available at: [Accessed 25/08/10].


Sex-Related Differences in Colour Vocabulary by Elaine Rich 

This paper describes an experiment designed to test the 

hypothesis that women have larger colour vocabularies 

than men. The results indicate that they do. The results 

also indicate that, in at least one social class, younger 

men have larger colour vocabularies than do older men. 

No such difference exists for women. However, a group 

of Catholic nuns did score lower than the rest of the 

women but still higher than the men. 

Introduction 

It is a widely held belief that women have larger 

colour vocabularies than do men. For example, Robin 

Lakoff (1975) states this as a fact and suggests as an 

explanation the observation that in this society women 

spend much more of their time on colour-related 

activities such as choosing clothes than men do. The 

purpose of our study was to see whether women really 

do use a wider array of colour terms than men do by 

presenting colours to both men and women, asking 

them to name them, and then measuring the size of the 

vocabularies they use. 

At least two related types of observations have been 

reported in the literature. The first deals with differences 

between men and women on other colour-related tasks; 

the second involves other differences between the 

language of men and that of women, suggesting that if 

men and women do differ in their vocabulary of colour, 

it would not be the only area in which their languages 

differ. 

The Wordswoth-Wells colour naming test (Wordsworth 

and Wells, 1911) tests the speed of recognition of 

standard colours. Subjects are presented with a card 

showing 100 patches of colour each 1 cm. square. Each 

patch is either red, yellow, green, blue, or black. The 

subject is timed as he names the colours of the patches 

in order. Wordsworth and Wells reported that among 

college students women do better at the task than men, 

i.e., they require less time. Ligon (1932) discovered that 

among children in grades one through nine girls do 

better on the Wordsworth-Wells test than do boys. He 

also showed that, except in the first two grades, the sex 

difference was greater on the colour-naming test than 

on a test of word reading designed to measure general 

verbal fluency, on which girls also did better than boys. 

This study shows that at least some of the differences 

between men and women are acquired at a very early 

age. 

There is a large amount of evidence that the language 

of women is not always the same as the language 

of men. The anthropological literature abounds in 

instances of sexual differentiation of language among 

so-called primitive people. Jespersen (1922) discusses 

the language of the Caribbeans of the small Antilles, in 

which about one tenth of the vocabulary is different for 

women than for men. The differences occur primarily 

in kinship terms, names for parts of the body, and also 

in isolated words such as friend, enemy, joy, work, war, 

house, garden, bed, poison, tree, sun, moon, sea, and 

earth. In Koasati, an American Indian language (Haas, 

1944), men’s and women’s speech differ in some forms 

of verbal paradigms. 

It has long been recognized that in English, men’s 

and women’s speech differ with respect to the use of 

swear words and euphemisms. There is evidence that 

other differences exist as well. Barren (1971) reports a 

difference between the speech of men and women in 

the relative frequency of various cases. 

This paper describes an experiment that was conducted 

to determine whether colour vocabulary is another area 

in which men’s and women’s speech differ. 

Procedure 

A set of 25 cards was constructed by colouring a twoinch 

square in the centre of each of 25 3x5 cards. The 

squares were coloured with single crayons selected from 

Crayola’s box of 64 crayons. No crayon was used more 

than once. 

Each subject was shown the cards one at a time and 

asked to state the word or phrase he would use to 

describe the colour. In order to standardize the task, 

each subject was told that he should imagine himself in 

the following situation:

“You have bought a shirt and now want to buy a pair of 

pants to match the shirt. You go into a store but haven’t 

got the shirt with you. You want to say to the salesperson, 

‘I have a —— shirt. Show me a pair of pants to 

go with it.’ “ 

The subjects were also told that they should attempt to 

describe the cards as independently as possible, that 

they should not compare them to each other, and that 

it was acceptable to give the same name to more than 

one card. 

The responses were recorded and then scored using a 

scheme designed to measure the extent of the subjects’ 

colour vocabularies. The responses were divided into 

four categories: 

(1) Basic— one of the following basic colour words: red, 

orange, yellow, green, blue, purple, violet, white, black, 

brown, grey, pink, tan. 

(2) Qualified — a basic word qualified by words such 

as light or dark or by another basic word, e.g. yellowish 

green. Responses in this category are more specific than 

basic responses but they do not actually show a larger 

vocabulary. 

(3) Qualified Fancy — a basic word qualified by special 

words, such as sky blue or hunter green. 

(4) Fancy — colour words not in the basic category, such 

as lavender, magenta, and chartreuse. 

A score for each subject was computed by assigning 

one point for each basic response, two for each 

qualified, three for each qualified fancy, and four for 

each fancy response. Since there were 25 cards, the 

possible scores range from 25 to 100. 

The subjects were divided into five groups on the basis 

of age, sex, and occupation as follows: 

Group I: men aged 20-35. Graduate students or people 

working in technical areas. 

Group II: men aged 45-60. All technically trained, highly 

educated professionals. 

Group III: women aged 20-35. Further divided into two 

groups: 


A: technical—corresponding to Group I. 

B: non-technical but well-educated. 

Group IV: women aged 45-60. Most of them married to 

the men in Group II. 

Group V: Catholic nuns. Most of them over 30. 

The Mann-Whitney U test (Siegel, 1956) was used 

to determine, on the basis of the observed scores, 

the probability that the scores of one group were 

stochastically higher than those of another group. 

The groups ranged in size from seven to 24 subjects. 

The size of the groups is taken into account in the Mann- 

Whitney test. 

Results 

Table 1 displays the median scores for each of the five 

groups. It suggests that: 

(1) Women use fancier words than men. 

(2) Younger men use fancier words than older men. 

(3) All the women have similar size vocabularies except 

the nuns, who use fewer fancy words than the other 

women. 

The Mann-Whitney test indicates that these differences 

are highly significant. Table 2 shows the significance 

levels obtained for the hypotheses that certain groups 

score higher than others. The following comparisons 

yielded no significant difference: 

(1) Technical v. non-technical young women. 

(2) Young women v. older women. 

Because the only significant difference among the 

women was between the nuns and the non-nuns, groups 

III and IV will be combined for the rest of this discussion. 

Table 3 shows the average number of times the 

members of each of the groups used each category 

of colour word. It shows that the women used more 

qualified fancy and fancy words than did the men, and 

the older men used significantly fewer fancy words than 

did the younger men. It also shows that the nuns used 

fewer fancy words than did the lay women.


Another measure of breadth of vocabulary is the 

number of times the same term was used to describe 

different colours. Table 4 shows the mean number of 

times a colour was described exactly the same way as 

a previous colour. The older men used the greatest 

number of repetitions, followed by the younger men, 

the nuns, and then the rest of the women. Thus both 

the fanciness score and the repeat count produce the 

same ordering of the groups. 

Discussion 

It was suspected at the start of the experiment that 

factors other than sex might have a significant effect 

on people’s colour vocabularies. For that reason, the 

groups were further subdivided by age and occupation. 

It is very difficult, however, to construct samples with 

no differences other than sex since, in this culture, 

sex is so highly correlated with other things. For 

example. Groups II and IV differ by sex, but also, not 

coincidentally, in the occupations of the people, the 

men working at technical jobs, the women having raised 

children. In fact, it has been assumed (for example, 

by Lakoff) that such sex-correlated differences are the 

reason for the differences in colour vocabulary. Women 

spend more time buying clothes and decorating living 

rooms. This study shows, however, that even when the 

principal occupation is the same (Group I v. Group IIIa) 

the women show a larger colour vocabulary than the 

men. 

The fact that the nuns score lower than the rest of 

the women also suggests that such cultural factors 

are significant. Not only do the nuns spend less time 

worrying about clothes (the ones in this experiment still 

wear habits) than do the other women, they are people 

who chose to give up such things. Both the fact that 

the nuns do score higher than the men and that women 

score higher than men, even if their current principal 

occupation is the same, suggest that this difference is 

determined quite early in life before adult occupations 

are chosen. 

The difference between the young men and the older 

men was surprising. There are at least two possible 

explanations for this observation. One is that the older 

men at one time had larger colour vocabularies but over 

the many years they have been married and therefore 

had someone else to buy their clothes and decorate 

their living rooms, their vocabularies have atrophied. 

The other explanation is that younger men have larger 

colour vocabularies than the older men ever had 

because sex stereotyping is dwindling in this society 

and men are increasingly interested in such things as 

clothes. The data obtained in this experiment provide 

no way to decide between the two. 

The goal of this experiment was to measure size of 

active vocabulary. It is difficult to do precisely that in 

an experimental situation where people are explicitly 

asked to name colours. Such a situation was necessary, 

however, in order to get each subject’s reaction to many 

different colours. The method chosen almost certainly 

produces a bias toward more exotic descriptions 

than the subjects would use in an everyday situation. 

However this bias is constant across all groups of 

subjects and should therefore not significantly affect the 

relative scores of the various groups. 

Conclusions 

The evidence collected in this experiment confirms the 

hypothesis that women have more extensive colour 

vocabularies than men. It also indicates that, at least 

in one social class, younger men have larger colour 

vocabularies than older men. 

References 

BARRON, N. (1971). Sex-typed language: the production 

of grammatical cases. Acta Sociologica, 14, 24-42. 

DUBOIS, P. H. (1939). The sex difference on the colornaming 

test. Amer. J. Psychol., 52, 380. 

HAAS, M. (1944). Men’s and women’s speech in Koasati. 

Language, 20, 142-9.

JESPERSEN, 0. (1922). Language: Its Nature, 

Development, and Origin (New York), chap. 13. 

LAKOFF, R. (1975). Language and Woman’s Place (New 

York). 

LIGON, E. M. (1932). A genetic study of color naming 

and word reading. Amer J Psychol 44 103-22. 

SIEGEL, S. (1956). Nonparametric Statistics for the 

Behavioral Sciences (New York). 

WOODWORTH, R. S. and WELLS, F. L. (1911). 

Association tests. Psychological Monographs, 57, 1-80. 


Reference: RICH, E., 1977. Sex-related differences in colour vocabulary. [online] Available at: [Accessed 

24/08/10].


Colour Zones – Explanatory Diagrams, Colour Names, and Modifying Adjectives by Paul Green-Armytage 

Abstract 

This paper presents a flexible system for describing 

colours which links everyday language to colour order 

systems. The structure, based on that of the Natural 

Colour System (NCS)1, has reference points which are 

defined and named. The structure is illustrated and an 

account is given of the processes used to establish the 

system of colour names and modifying adjectives which 

identify the reference points. 

1. Introduction- Colour Zones 

Colour Zones are subdivisions of the 3-D colour solid. 

Each zone contains a range of similar colours with a focal 

colour as a reference point at the centre of the zone. All 

the colours in a given zone have the same identification. 

1.1 Three Levels of Precision. 

The smaller the zones, the narrower the range of 

different colours in each zone, the larger the number 

of zones needed to fill the colour solid, and the greater 

the precision. A primary source of inspiration, which 

gave impetus to this project, was the Universal Color 

Language (UCL)2 with its six levels of precision. A 

simpler alternative to the first three levels of the UCL 

was one of the objectives of the Colour Zones project. 

At level one there are six zones, the focal colours of 

the zones being the six Elementary Colours as defined 

by Ewald Hering3 which form the basis of the NCS: 

White, Black, Yellow, Red, Blue, and Green. Further 

subdivisions provide 27 zones at level two and 165 

zones at level three. 

1.2 Merits of Imprecision – the Post-it Advantage 

Challenging assumptions is one of the strategies 

recommended by Edward de Bono4 for generating 

new ideas. If we assume that the value of a glue is to be 

measured by how firmly it sticks we might not recognise 

the advantage of having a glue that does not stick very 

well. In their humble way, Post-it notes have changed 

the world. A common assumption about colour order 

systems is that they should be as precise as possible, 

but I believe that a system which is imprecise and 

flexible can offer what I call the Post-it advantage. If the 

hit-and-miss of everyday language can be so imprecise 

as to be the equivalent of no glue at all, and a colour 

order system is the equivalent of Supa-Glue, it can be 

useful to have something in between – the equivalent 

of Post-it – a fuzzy system with a somewhat elastic 

structure. 

2. Structure 

The colour solid of the NCS provides the structural 

skeleton for the Colour Zones. It can be presented in 

two projections – a plan view (a colour circle which 

shows the sequence of hues) and part cross section (a 

colour triangle which shows the set of nuances). Hering’s 

Elementary Colours are the focal points of the six zones 

at level one. Anders Hård5 has drawn attention to the 

distinctive character of colours with equal resemblance 

to Elementary Colours such as a grey that is equally 

whitish and blackish. These equal resemblance colours 

together with the Elementary Colours provide focal 

points for the 27 zones at level two. Further subdivision, 

with intermediate colours as additional focal points, 

results in the 165 zones at level three. 

3. Identifying the Zones 

Colour names are used to identify colours in the zones 

at levels one and two. Colour names, used singly or 

in pairs, and with modifying adjectives, are used for 

the more precise descriptions required for the smaller 

zones at level three. Research was undertaken to find 

a set of colour names and modifying adjectives which 

reflect current usage and can be used to describe 

colours in this way. A definitive study, with a large 

number of participants randomly selected from different 

populations within the English speaking world, was 

beyond my resources. So I can claim no more than 

provisional status for my findings. My objective was a 

set of words that people would accept and that I could 

defend. 

4. Colour Names 

Colour is a sophisticated concept which requires the 

ability to make connections between things that are 

otherwise quite different from one another. Most, if 

not all words that are used as colour names in English 

were first used for something else, ‘orange’ being a

clear example. In some cases the original meaning has 

got lost in history. Philologist Anna Partington6 has 

traced the word ‘red’ through its migrations from one 

language to another to an ancient word for ‘blood’. This 

seems a likely scenario: Someone notices a similarity in 

appearance between blood and a particular flower and 

describes the flower as ‘blood-like’. With increasing use 

of the expression it becomes possible to describe the 

flower simply as ‘blood’ – a colour name has been born. 

4.1 Basic Colour Terms – Past, Present, and Future 

Research, which began with the study by Brent Berlin 

and Paul Kay7, has provided convincing arguments for 

a set pattern of language evolution; there are strict 

limitations to the order in which languages acquire 

‘basic colour terms’. Basic colour terms are best defined 

as ‘the smallest subset of color terms such that any color 

can be named by one of them’8. In today’s English these 

are white, black, red, green, yellow, blue, brown, grey, 

purple, orange, and pink. But today’s smallest subset 

was once smaller. In 1721 Nathaniel Bailey’s dictionary9 

did not recognise orange or pink as colour names with 

today’s meanings; what is ‘pink’ today would then have 

been just another kind of red. If the smallest subset was 

once smaller, presumably it may also get bigger. The 

Colour Zones project is an attempt to establish a kind 

of ideal subset for the future. The level two subdivision 

offers a manageable range of 27 colour zones in a 

coherent structure with well defined focal points. The 

idea is to boost the number of basic terms from eleven 

to 27. Today’s eleven terms are spread unevenly over 

those 27 zones, with eight zones that would be ‘green’ 

and only one ‘pink’. If these terms are now restricted 

to the zones which suit them best, new names can 

be provided for the remaining 16 zones. The most 

suitable names will be those which come most readily to 

people’s minds and which are commonly applied to the 

colours in question. Several studies were conducted. 

4.2 Salience Test 

Responses from 247 people to the request that they 

write down as many colour names as they could think 

of in five minutes produced 216 names that were listed 

by more than one person. Of these, 183 are recognised 


as colour names in more than one of the six current 

dictionaries consulted. A rank order of salience for the 

names was established by the number of people who 

listed each one. 

4.3 Preference Test 

The 27 focal colours were shown to a group of eleven 

people who were asked to propose names for the 

colours. This exercise confirmed earlier ideas about 

which colours should become the limited domains of the 

existing eleven basic terms. From the names proposed 

for the other 16 colours, 32 names were chosen as 

potentially most suitable. Colours and candidate names 

were then shown to several groups. Responses from 148 

people established orders of preference where there 

was more than one candidate name for a given colour 

zone. 

4.4 Correspondence Test 

An indication of the range of colours to which each 

name might be applied was established by asking 

people to choose the best example for each candidate 

name from all the colours in the NCS atlas. Responses 

from 54 people established the extent to which each 

name was on target, the ideal situation being that all 

colours chosen would fall within the zone to be named. 

4.5 Conversation Test 

To test the credibility of the names as colour names, 

people were asked to imagine this fragment of 

overheard conversation: “We saw it on television, it was 

…..”. Responses from 35 people established the extent 

to which candidate names might be used to complete 

that sentence and be recognised as names for colours 

as opposed to names for other things. 

4.6 Decisions – Strong Claims, Conflicting Claims, and 

Problem Colours 

Decisions were made. The key consideration was the 

correspondence test. A name could only be accepted 

if the majority of colours chosen for it were within the 

zone to be named. For some colours the choice was 

easy: lemon, khaki, lime, olive, aqua, turquoise and 

navy were generally well supported by the data and


scored better than any rival names in the tests. For other 

colours there were two names with competing claims. In 

such cases the first appeal was to the correspondence 

test. So apricot was chosen ahead of peach, and teal 

ahead of jade. But there was nothing in that test to 

separate maroon from burgundy, mauve from lilac, 

or azure from sky. Although burgundy and lilac were 

preferred by more people, maroon and mauve were 

more salient and scored better in the conversation test. 

Although less appealing on aesthetic grounds, maroon 

and mauve are clearly better established as colour 

names and so were chosen. Azure was chosen ahead of 

sky because it is a complete colour name. Unlike navy, 

sky cannot yet stand alone, it still has to be sky blue. 

Four problem colours remained. Mint and forest came 

out of nowhere as names for light and deep green. 

They are barely established as colour names, but were 

clear winners in the preference and correspondence 

tests. Chartreuse, though unfamiliar to many, is an 

established colour name and the best option for light 

yellow-green. For deep purple the best option seemed 

to be grape, but with green grapes and purple grapes 

there was ambiguity which was born out by two clusters 

in the correspondence test. At the last minute I came 

across aubergine and chose it although it has not been 

subjected to all the tests. 

5. Modifying Adjectives 

For the greater precision at level three the 27 zones of 

level two contract around their focal points and new 

zones are formed in the gaps. It might be possible to 

find or invent 165 colour names but their use would be 

neither practical nor in line with normal use of language. 

We combine names, e.g. yellow-orange, to add 

precision in terms of hue. And we introduce modifying 

adjectives like pale and deep to add precision in terms 

of nuance. Further studies were conducted to find a 

usable set of modifying adjectives to identify differences 

in nuance. 

5.1 Salience Test for Adjectives 

As with colour names, people were asked to list as 

many modifying adjectives as they could think of in 

five minutes. In responses from 106 people, 161 words 

were listed by more than one person and a rank order 

of salience for these words was established. Of these 

words, the 32 most salient and most potentially useful 

were selected for further testing. 

5.2 Correspondence Test for Adjectives 

An indication of how modifying adjectives might be 

used to describe colours according to their variations 

in nuance was established by asking people to use a 

seven point scale to rank the appropriateness of each 

adjective as a description for each of a given set of 

colours. Ten colours of similar hue were presented 

together. Four sets were used, one for each of the 

elementary hues. Responses from 30 people made it 

possible to derive patterns which can be used to justify 

the choice of specific adjectives for specific nuances. 

5.3 Preference Test. 

Adjectives for describing colours in general and colours 

that are also named The four sets of ten colours were 

arranged in random order in a square grid of 40 colours. 

From the list of candidate adjectives people were asked 

to select the most appropriate as a description for 

each colour. While a preference order could then be 

established, responses from 41 people made it clear 

that the choice of adjective could depend on whether 

or not the colour was also named – in a comparative 

situation a ‘light blue’ might or might not also be a ‘light 

colour’. It is possible to refer to a group of unnamed 

colours as, e.g. ‘pale colours’ or ‘deep colours’, but when 

a specific colour is also named, the role of modifying 

adjectives is somewhat different. An adjective indicates 

relative appearance. E.g. focal pink belongs in the ‘light’ 

zone. In relation to focal pink a given pink might be 

judged to be pale, dull, deep or strong. 

5.4 ‘Road test’. 

Adjectives and names combined To test whether a finite 

set of names and adjectives could be used by different 

people to describe colours consistently and with some 

degree of precision in normal conversation, the grid of 

40 colours was presented to another group. This time 

the choice of names and adjectives was limited to lists 

which were now all but finalised. Responses from 32

people using this system for the first time (some under 

protest!) did result in some degree of consensus. 

6. Conclusion 

The three levels of Colour Zones, with the reference 

points identified by names and adjectives, are shown in 

figures 1 – 3. While the underlying structure of Colour 

Zones is quite regular, it clearly needs to be able to 

bend and stretch if it is to accommodate everyday 

language. And it has to admit alternate descriptions for 

the same colour. The colour names themselves are not 

all entirely satisfactory and there are complications in 

the way people use modifying adjectives which threaten 

to undermine the simplicity of Colour Zones as a system. 

However, the degree of consensus shown in the way 

people used names and adjectives when their choice 

was restricted suggests that the system could serve its 

intended purpose. It could be like Post-it, a glue that 

does stick although it does not stick very well. I hope it 

may also ease the transition from use of unstructured 

language to use of a colour order system and make it 

easier for people to describe colours in a way that can 

be related to the NCS without needing to master that 

system’s letter/number notation code. 


Reference: GREEN-ARMYTAGE, P., 2002. Colour Zones – Explanatory diagrams, colour names, and modifying adjectives. [online] Available at: [Accessed 16/05/11].


How Many Colours Should be in the Rainbow? by Ronaldo Martins 

Abstract 

This paper addresses the color categorization 

problem from the multicultural knowledge 

representation perspective. Given the fact that 

languages employ different color naming strategies, 

two questions are examined: 1) which colors should 

be represented in a multilingual and cross-cultural 

knowledge base? and 2) how to represent them to 

assure reciprocal translatability? Three different, 

although network-like structured, knowledge 

representation formalisms are considered as candidate 

alternatives: Conceptual Graphs (CG), Resource 

Description Framework (RDF) and Universal Networking 

Language (UNL). It is claimed that UNL, better than the 

others, can cope with multilingualism and cross-cultural 

color representation schemes. 

Introduction 

It is been widely observed that languages contain 

different numbers of color names and carve up the color 

spectrum in different ways [1,2]. In English, for instance, 

Sir Isaac Newton identified seven different colors in 

the rainbow: red, orange, yellow, green, blue, indigo 

and violet (or purple). In Shona, a language spoken by 

nearly 80% of people in Zimbabwe, there seems to be 

only three: [Cipswuka], which roughly corresponds to 

the English ‘purple’, ‘orange’ and ‘red’ (i.e., the borders 

of the color spectrum); [Citema], which covers part of 

both English ‘blue’ and ‘green’; and [Cicema], part of the 

‘green’ and most of the ‘yellow’ [3]. For the Dani people 

of Papua New Guinea, only two basic color terms have 

been reported: [mili], for “cold, dark colors”; and [mola], 

for “warm, light colors” [4]. 

Such diversity has been often addressed as a proof for 

either cultural relativism [1,5] or biological determinism 

[3,4,6]. In the former case, language is believed to 

govern perception and determine world structure; in 

the latter, there would be color universals, provided that 

regularities in the pattern of color naming would have 

been noticed across different languages. 

In what follows, the so-called lexical color categorization 

problem will be referred to in a rather different 

perspective: given the fact that there is no possible 

one-to-one mapping between color names of English, 

Shona and Dani, two questions must be addressed in 

the field of Knowledge Representation: 1) which colors 

should be represented (i.e., named) in a multilingual and 

cross-cultural knowledge base?; and 2) how to represent 

them, to assure reciprocal translatability between 

different color naming strategies? 

In order to answer these two questions, this paper 

will consider and compare three different knowledge 

representation formalisms: Sowa’s Conceptual Graphs 

(CG); W3C’s Resource Description Framework (RDF); and 

the Universal Networking Language (UNL). Although all 

of them are semantic networks, they are supposed to 

adopt different strategies for representing knowledge. 

This paper is divided in two different parts: in the first, 

the color representation problem will be addressed 

inside each analyzed formalism: CG (Section 1), RDF 

(Section 2) and UNL (Section 3). The second part 

provides a very brief comparative analysis (Section 4) 

and draws a partial conclusion (Section 5). 

Conceptual Graphs 

Conceptual graphs (CGs) [7,8] are said to be “a 

system of logic based on the existential graphs of 

Charles Sanders Peirce and the semantic networks of 

artificial intelligence”. They would express meaning 

in form that is “logically precise, humanly readable, 

and computationally tractable”. CGs have been 

implemented in a number of projects for information 

retrieval, database design, expert systems, and natural 

language processing. 

CGs represent meaning sentence by sentence, as a 

bipartite graph where every arc links 1) a concept node, 

represented by rectangles (or square brackets), and 2) a 

conceptual relation node, represented by circles or ovals 

(parenthesis). In CGs linear form, the English ‘the sky is 

blue’ would be represented either as (1), (2) or (3) below, 

depending on the type hierarchy defined by the user:

(1) [sky] – (Att) –> [blue] 

(2) [sky] – (Att) –> [color: blue] 

(3) [sky] – (Color) –> [blue] 

Given that CGs are mathematical structures, they 

impose no commitments to any concrete notation or 

implementation. In this sense, the set of conceptual 

relation nodes (‘att’), as well as the set of concept nodes 

(‘sky’ and ‘blue’), are not a part of the formalism itself, 

which is rather a very abstract syntax for knowledge 

representation. 

If we consider the cross-linguistic lexical matching 

problem referred to in the Introduction, the CG 

formalism, because of its excessive power, will not be of 

much help. Neither (1), (2) nor (3) brings any clue to the 

fact that, in this context, but maybe not in others, ‘blue’ 

is to be translated as [Citema] and [mili] in Shona and 

Dani, respectively. 

The user can obviously refine the semantic granularity of 

‘blue’, as to refer to different instances of it, according 

to the wavelength, for instance: [blue:#436nm], 

[blue:#437nm], [blue:#438nm], etc. As the Shona and 

Dani vocabularies would also be indexed to the same 

scale, one could easily precise the intersection between 

any language reference, to provide precise translations. 

Nevertheless, there is no clear way how to get to such 

a precise reference, i.e., how to define the wavelength 

intended by the English ‘blue’ in a phrase like ‘the sky 

is blue’. It can range from 430nm to 490nm, and still 

fall out the range intended by [Citema], which goes 

from 440nm to 510nm. The problem, thus, is not only 

a matter of lexical matching, but mainly a problem of 

crossing world references, which are supposed to be 

different to an English and a Shona speaker. Given the 

same circumstances, a Shona speaker may come across 

the idea that the sky is rather ‘green’. 

Resource Description Framework 

Resource Description Framework (RDF) [9] is a 


framework for describing and interchanging metadata. 

It is a general purpose language for representing 

(meta-)information in the Web, and it has been the 

backbone of the Semantic Web initiative, devised by the 

W3C to improve information retrieval and knowledge 

processing. An expression in RDF is a directed labeled 

graph, which consists of nodes (resources, literals or 

blanks) and labeled directed arcs (properties) that link 

pairs of nodes. A resource in RDF is anything that can 

have a URI (Uniform Resource Identifier). This includes 

web pages, as well as individual elements of an XML 

document. Formally, as in the case for URL, it is a 

compact string of characters for identifying an abstract 

or physical resource. A property is a resource that has 

a name and can be used to identify the relationship 

between the nodes connected by the arc. As the arc 

label may also be a node in the graph, any property can 

have its own properties. Any statement in RDF consists 

of a resource (the ‘subject’), a property (the ‘predicate’) 

and a value (the ‘object’). The value can just be a 

string, or it can be another resource as well. There is a 

straightforward method for expressing this in XML: 

 

OBJECT 

 

In the case for ‘The sky is blue’, the use of RDF can be 

somewhat misleading, in the sense it is a language for 

representing meta-knowledge rather than knowledge 

itself. However, it should be stressed that, in many 

circumstances, ‘The sky is blue’ can be said to share 

the structure of ‘The author of ‘http://www.undl.org’ is 

‘UNDL Foundation’’, which seems to stand for a kind 

of knowledge more frequently represented by means 

of RDF. Accordingly, there should be possible to take 

both ‘sky’ and ‘blue’ as different resources (with specific 

URIs), to be linked by a property of ‘color’. In this case, 

the knowledge conveyed by ‘The sky is blue’ could be 

expressed by (4):


(4) 

 

blue 

 

If we take the predicate ‘color’ to be a class in a RDF 

vocabulary, and the value ‘blue’ to be another resource, 

rather than a string, the RDF statement would be (5): 

(5) 

 

 

 

As we consider the lexical matching problem between 

English, Shona and Dani, we could both say (6) and (7) 

below: 

(6) 

 

[Citema] 

 

(7) 

 

 

 

These solutions obviously do not allow for solving any 

possible mismatching as referred to above. Yet one can 

associate some hues of ‘blue’ to some hues of [Citema] 

in some web ontology language, such as OWL, there 

is no possible way of representing the information that 

‘blue’ can be either [citema] or [Cipswuka], and that 

[Citema] can be either ‘blue’ or ‘green’, depending on 

the context. 

Additionally, as RDF properties and classes are not 

built-in and must be defined by vocabularies (such as 

the Dublin Core, for instance), even the conception that 

‘blue’ and [citema] are ‘colors’ may be defied. 

Universal Networking Language 

The Universal Networking Language (UNL) [10] is 

an “electronic language for computers to express 

and exchange every kind of information”. In UNL, 

information is also represented sentence by sentence, 

as a hyper-graph defined by a set of directed binary 

labeled links (relations) between nodes (Universal 

Words, or simply UW), which stand for concepts. UWs 

can also be annotated with attributes representing 

subjective, mainly deictic, information. 

In UNL, the English sentence ‘The sky is blue’ would be 

represented as a single binary relation: 

(8) aoj(blue(icl>color).@entry, sky(icl>natural world)) 

In this expression, ‘aoj’ is a relation (standing for ‘thing 

with attribute’), ‘blue(icl>color)’ and ‘sky(icl>natural 

world)’ are UWs, and ‘@entry’ is an attribute. Differently 

from CG and RDF, UNL relations are built in the very 

formalism. They constitute a fixed 44-relation set and 

convey information on ontology structure (such as 

hyponym and synonym), on logic relations (such as 

conjunction and condition) and on semantic case (such 

as agent, object, instrument, etc). The attribute set, 

which is subject to increase, currently consists of 72 

elements, which cope with speaker’s focus, attitudes, 

viewpoints, etc., towards the event. The set of UWs, 

which is open, can be extended by the user, but any UW 

should be also defined in the UNL KB through a master 

definition (MD). 

In the UNL approach, the solution for the lexical color 

categorization is rather different from the others. There 

could be concurrently ‘blue(icl>color)’, ‘green(icl>color)’, 

‘citema(icl>color)’, ‘cispwuka(icl>color)’, ‘mili(icl>color)’, 

‘mola(icl>color)’, etc. These UWs could belong to the 

UW Dictionary all together, as long as they were defined 

in the UNL KB. In this sense, a Shona and a Dani speaker 

may use

(9) or (10) below instead of (8): 

(9) aoj(citema(icl>color).@entry, sky(icl>natural world)) 

(10) aoj(mili(icl>color).@entry, sky(icl>natural world)) 

Accordingly, there is no need to reduce ‘blue’ to ‘citema’ 

or vice-versa. Both can be kept, because they represent 

different concepts. 

Translating English into Shona or into Dani may seem, 

at this extent, rather impossible, at least inside the 

UNL approach. But UNL is claimed not to be a mere 

interlingua; it should rather be placed either at the 

target or at the source position in a bilingual MT system. 

For that reason, there is no commitment for (8), (9) and 

(10) to be the same. 

The Shona version of ‘The sky is blue’ would be 

translated (enconverted) into UNL as (9) above because, 

in the UNL approach, analysis is supposed to be totally 

independent from any generation process or target 

language other than UNL itself. To translate [Citema] 

as ‘blue’ is to perform an English-driven translation 

movement, i.e., to carry out a Shona analysis from the 

English perspective. In a Shona-to-Dani translation, 

such correspondence would be not only pointless but 

even harmful, as [Citema] is completely comprised 

under [mili]. Therefore, the UNL expressions (8), (9) and 

(10), although provoked by very similar settings, are 

not planned to be the same, because their reference 

is not exactly the same, otherwise UNL would impose, 

over Shona and Dani, an English bias, and cultural 

differences, as well as non-English-mediated similarities, 

would be lost. 

As a result of this difference-preserving analysis 

strategy, the English generation out of (8), (9) and (10) 

may lead to (8a), (9a) and (10a) referred to below: 

(8a) The sky is blue. 

(9a) The sky is citema. 

(10a) The sky is mili. 


One may claim that (9a) and (10a) are not English yet 

and could not be understood by English speakers. 

This is true, but from (11) and (12) below, which stand 

for entries in the UNL KB, (9b) and (10b) can be 

automatically derived: 

(11) ‘citema(icl>color)’ definition in the UNL KB 

icl(citema(icl>color), color(icl>property)) = 1; 

aoj(between(aoj>thing,obj>thing), citema(icl>color)) = 1; 

obj(between(aoj>thing,obj>thing), green(icl>color)) = 1; 

fmt(green(icl>color), blue(icl>color)) = 1; 

(12) ‘mili(icl>color)’ definition in the UNL KB 

icl(mili(icl>color), color(icl>property)) = 1; 

aoj(dark(aoj>color), color(icl>property)) = 1; 

aoj(cold(aoj>color), color(icl>property)) = 1; 

(8b) The sky is of a color in between green and blue. 

(9b) The sky is of a cold, dark color. 

This is certainly English, and much better translation 

than just ‘The sky is blue’, which may not convey the 

meaning intended by the Shona and Dani speaker. 

A Brief Comparative Analysis 

The main differences between CG, RDF and UNL seem 

to concern substance rather than form. All of them are 

semantic networks represented by hyper-graphs, i.e., 

a set of links between nodes. In RDF and UNL, these 

links are labeled; in CG, they are not, but there can be 

relational nodes. Yet they are very similar, there are at 

least three striking differences in the UNL perspective: 

1) the set of labeled links (or relational nodes) is part 

of the very formalism; 2) nodes can be annotated by 

attributes; and 3) in spite of the name “Universal Word”, 

nodes are not supposed to be universal, and can be 

language-dependent, as long as they are associated to 

each other in the UNL KB. At least in the case for color 

naming, this latter commitment seems to be decisive. 

Color names cannot be conflated, and difference 

preserving strategies should be an asset in knowledge 

representation formalisms. Although this solution may 

place a heavy burden on the generation (deconverting)


process, it should be stressed that, in the UNL System, 

1) the UW definition in the UNL KB is supposed to 

be registered by the own UW author (the Shona and 

the Dani speaker, for instance); and 2) the UNL KB is 

supposed to be a distributed knowledge base, to be 

hosted in a remote language server, to be accessed 

worldwide by the internet. This can be taken to alleviate 

the requirements for the UNL-to-natural language 

generation system. 

It should be stressed that this does not mean that CG 

or RDF are not able to representing color naming in 

different cultures. Actually, as they constitute mere 

formalisms, rather than a theory of knowledge, they 

can be adapted to represent the same as UNL. The 

difference, thus, is not a matter of expressive power, 

but of use. Although they can be said to preserve 

cultural differences, they have been mainly used for 

truth-preserving purposes, which is rather difference 

conflating. In this sense, the knowledge conveyed by 

CGs and RDFs is rather encapsulated and culturedependent. 

Yet concepts might stand for semantic 

primitives whose validity is said to be universal, in both 

frameworks the language bias is somewhat unavoidable. 

Foreign concepts are useful if and only if they can be 

reduced to some conceptual primitive that is defined 

inside a given language (normally English). In UNL this 

seems not to be the case. 

Towards a Conclusion 

Irrespective of the nature of the color naming process, 

whether cultural or natural, whether evolutionary or not, 

there is no possible one-to-one mapping between color 

names of English, Shona and Dani. Consequently, the 

answer to the two questions addressed in the beginning 

is the paper would be the following: 1) any color name 

should be represented in a really multilingual and crosscultural 

knowledge base, no matter how languagedependent 

the color name can be. This should be 

referred to as the comprehensiveness commitment of 

any KB; 2) in order to allow for reciprocal translatability 

between different color naming strategies, these color 

names should be associated to each other by means of 

complex (relational) definitions. This can be said to be 

the self-consistency commitment of any KB. As seen 

above, at least for the time being, it seems that only 

UNL, for its difference-preserving structure, can really 

cope with both the comprehensiveness and the self 

consistency engagements. 

References 

[1] Whorf, B. L. “Science and Linguistics”, Technology 

Review 42: 229- 231, 1940. 

[2] Lucy, J. A, “The linguistics of color”, Color, 

Categories in Thought and Language, Hardin, C. L. 

and Maffi, L. (eds), Cambridge, England, Cambridge 

University Press, 1997. 

[3] Dowling, J.E., The Retina: an approachable part of 

the brain, The Belknap Press, Harvard University Press, 

Cambridge, Massachusetts, 1987. 

[4] Rosch, E, “Universals in color naming and memory”, 

Journal of Experimental Psychology 93: 1-20 

[5] Sapir, E, Language, New York, Harcourt, Brace, 1921. 

[6] Brent, B. and Kay, Paul, Basic Color Terms: Their 

Universality and Evolution, Berkeley, University of 

California, 1969. 

[7] Sowa, J. F., Conceptual Structures: Information 

Processing in Mind and Machine, Addison-Wesley, 

Reading, MA, 1984. 

[8] Sowa, J. F., Knowledge Representation: Logical, 

Philosophical, and Computational Foundations, Brooks 

Cole Publishing Co., Pacific Grove, CA, 2000. 

[9] Lassila, O. and Swick, R. R. (eds.), Resource 

Description Framework (RDF): model and syntax 

specification. W3C Recommendation 22 February 1999. 

[10] Uchida, H., Zhu, M.., and Della Senta, T. A gift for a 

millenium, Tokyo, IAS/UNU, 1999. 

Reference: MARTINS, R., 2003. How Many Colours Should be in the Rainbow? [online] Available at: [Accessed 28/11/10].


In modern Hebrew, the word “כתום” (pronounced / 

kaˈtom/) is generally used to refer to orange (the color 

of the fruit), although it is also applied to colors closer to 

yellow or gold based on its biblical usage as golden. 

Indo-European 

Baltic 

There are separate words for green (zaļš) and blue (zils) 

in Latvian. Both zils and zaļš stem from the same Proto- 

Indo-European word for yellow (*ghel). Several other 

words in Latvian have been derived from these colors, 

namely grass is called zāle (from zaļš), while the name 

for iris is zīlīte (from zils). 

The now archaic word mēļš was used to describe both 

dark blue and black (probably indicating that previously 

zils was used only for lighter shades of blue). For 

instance, blueberries are called mellenes. 

Slavic 

Bulgarian, a South Slavic language, makes a clear 

distinction between blue (синьо, sinyo), green (зелено, 

zeleno) and black (черно, cherno). 

In the Polish language, blue (niebieski) and green 

(zielony) are treated as separate colors. The word for sky 

blue or azure—błękitny—might be considered either 

a basic color or a shade of blue by different speakers. 

Similarly dark blue or navy (granatowy — deriving from 

the name of pomegranate (granat), some cultivars of 

which are dark purplish blue in color) can be considered 

by some speakers as a separate basic color. Black 

(czarny) is completely distinguished from blue. As in 

English, Polish distinguishes pink (“różowy”) from red 

(“czerwony”). 

The word siwy (possibly a Finnish loanword) means 

blue-gray in Polish (literally it means the color of gray 

hair). The word siny refers to violet-blue and is used 

to describe the color of bruises (“siniaki”), hematoma, 

and the blue skin discoloration that can result from 

moderate hypothermia. 

Russian does not have a single word referring to the 

whole range of colors denoted by the English term 

“blue.” Instead, it traditionally treats light blue (голубой, 

goluboy) as a separate color independent from plain or 

dark blue (синий, siniy), with all seven “basic” colors of 

the spectrum (red - orange - yellow - green - голубой / 

goluboy (sky blue, light azure, but does not equal cyan) - 

синий / siniy (‘true’ deep blue, like synthetic ultramarine) 

- violet) while in English the light blues like azure and 

cyan are considered mere shades of “blue” and not 

different colors. To better understand this, consider 

that English makes a similar distinction between “red” 

and light red (pink, which is considered a different color 

and not merely a kind of red), but such a distinction 

is unknown in several other languages; for example, 

both “red” (红 / 紅, hóng) and “pink” (粉红, fěn hóng, 

lit. “powder red”) have traditionally been considered 

varieties of a single color in Chinese. Russian language 

as well makes distinction between red (красный, 

krasniy) and pink (розовый, rozoviy). 

Similarly English descriptions of rainbows have often 

distinguished between blue or turquoise and indigo, 

the latter of which is often described as dark blue or 

ultramarine. 

Blue: plavo (плаво) indicates any blue 

• Dark blue: modro 

• Navy blue: teget 

• Steel-blue: čelikasto-ugasita† (used in reference to 

the flag of the Serbian revolution) 

• Light blue: sinja 

• Lighter blue: plavetna† (used in reference to the flag 

of Montenegro) 

• Green: zeleno (зелено) 

† Not used in everyday language. 

Other shades are presented with a preceding word i.e. 

tamnoplava. 

Blond hair is called plava (blue). Anything that is 

turquoise is called green. Sometimes blue eyes are also 

called green eyes.

Celtic 

The boundaries between blue and green are not the 

same in Welsh and English. The word glas is usually 

translated as “blue”. It can also refer, variously, to the 

color of the sea, of grass, or of silver. The word gwyrdd 

is the standard translation for “green”. 

Glas (same spelling) is, comparably, the translation for 

“green” in Irish and Breton, with specific reference to 

plant hues of green; other shades would be referred to 

in Modern Irish as uaine or uaithne. In Middle Irish and 

Old Irish, glas was a blanket term for colors ranging from 

green to blue to various shades of grey (i.e. the glas of a 

sword, the glas of stone, etc.). 

In Modern Irish the word for “blue” is gorm – a 

borrowing of the Early Welsh word gwrm, now obsolete, 

meaning “dark blue” or “dusky”. A relic of the original 

meaning (“dusky”) survives in the Irish term daoine 

gorma, meaning “Black people”. 

Contemporary Scottish Gaelic distinguishes between 

blue and green with the terms gorm and uaine, 

respectively. However, the dividing line between the 

two colors is somewhat different from English, with 

uaine signifying a light green or yellow-green. The word 

gorm extends from dark blue (what in English might 

be Navy blue) to include the dark green or blue-green 

of vegetation. Grass, for instance, is gorm, rather than 

uaine. In addition, liath covers a range from light blue to 

light grey. 

In traditional Welsh (and related Celtic languages), glas 

could refer to blue but also to certain shades of green 

and grey; however, modern Welsh is tending toward the 

11-color Western scheme, restricting glas to blue and 

using gwyrdd for green and llwyd for grey. Similarly, in 

Irish, glas can mean various shades of green and grey 

(like the sea), while liath is grey proper (like a stone), 

and the term for blue proper is gorm (like the sky or 

Cairngorm mountains), although gorm can also in some 

contexts mean black — sub-Saharan black people 

would be referred to as daoine gorma, or blue people. 


Also the boundary between colors varies much more 

than the “focus point”: e.g. a single island is named in 

Breton Enez glas (“the blue island”) and in French l’Ile 

Verte (“the green island”) referring in both cases to the 

greyish-green color of its bushes, even though both 

languages distinguish green (as in lawn grass) from blue 

(as in a cloudless midday sky). 

Romance 

The Romance terms for “green” (French vert) etc. are 

all from Latin viridis. The terms for “blue”, on the other 

hand, vary: French bleu is from Germanic, and was 

in turn loaned into many other languages, including 

English. 

Italian distinguishes blue (blu) and green (verde). There 

are also two words for light blue (e.g. sky’s color): 

azzurro and celeste. Azzurro, the English equivalent of 

“Royal Blue”, is not considered to be a shade of blu, 

but rather the opposite, i.e. blu is a darker shade of 

azzurro. Celeste literally means ‘(the color) of the sky’ 

and is often used as synonym of azzurro, although it’s a 

lighter color than azzurro. To indicate a mix of green and 

celeste Italians say verde acqua, literally water green or 

acquamarina (aquamarine), or glauco, which is also used 

to indicate a mix of green and gray in plants. 

In Portuguese, the word “azul” means blue and the 

word “verde” means green. Furthermore, “azul-claro” 

means light-blue, and “azul-escuro”, means dark-blue. 

More distinctions can be made between several hues of 

blue. For instance, “azul-celeste” means sky blue, “azulmarinho” 

means navy-blue and “azul-turquesa” means 

turquoise-blue. One can also make the distinction 

between “verde-claro” and “verde-escuro”, meaning 

light and dark-green respectively, and more distinctions 

between several qualities of green: for instance, “verdeoliva” 

means olive-green and “verde-esmeralda” means 

emerald-green. Cyan is usually called “ciano”, but can 

also be called “verde-água”, meaning water green, or 

“azul-piscina”, meaning pool blue.


Romanian clearly distinguishes between the colors 

green (verde) and blue (albastru). It also uses separate 

words for different hues of the same color, e.g. light 

blue (bleu), blue (albastru), dark-blue (bleu-marin or 

bleomarin), along with a word for turquoise (turcoaz) and 

azure (azur or azuriu). 

Similarly to French, Romanian, Italian and Portuguese, 

Spanish distinguishes blue (azul) and green (verde) and 

has an additional term for the tone of blue visible in the 

sky, namely “celeste”, which is nonetheless considered a 

shade of blue. 

Germanic 

In Old Norse the word blár was also used to describe 

black (and the common word for people of African 

descent was thus blámenn ‘blue/black men’). In Swedish, 

blå, the modern word for blue, was used this way until 

the early 20th century. 

German and Dutch distinguish blue (respectively Blau 

and blauw) and green (Grün and groen) very similar 

to English. There are terms for light blue (Himmelblau 

and hemelblauw, literally ‘sky blue’) and darker shades 

of blue (Dunkelblau and donkerblauw). Note that in 

German all nouns are capitalized, therefore color names 

are written with a capital letter when appearing as 

a noun, but with a lowercase letter when used as an 

adjective. In addition, adjective forms of most traditional 

color names are inflected to match the corresponding 

noun’s case and gender. A number of “modern” color 

names (such as rosa, meaning ‘pink’ or ‘rose’) are not 

inflected; instead, in Standard German, it is necessary 

to add the suffix farben or farbig (colored), and inflect 

the result (for example: ein rosafarbenes Auto, lit. ‘a 

pink-colored car’). This, however, is often disregarded 

in colloquial speech, resulting in forms like ein rosanes 

Auto or simply ein rosa Auto. 

Greek 

The terms for “blue” and “green” have changed 

completely in the transition from Ancient Greek to 

Modern Greek. Ancient Greek had γλαυκός “bluish 

green, blue-green”, contrasting with χλωρός “yellowish 

green”. Modern Greek has πράσινο (prásino) for green, 

and the recent loan μπλε (ble

The color of the sky is variously described in Persian 

poetry using the words sabz, fayruzeh, nil, lājvardi, or 

nilufari— literally ‘green’, ‘indigo’, ‘turquoise’, ‘azure’, or 

‘the color of water lilies’. For example, sabz-ākhor ‘green 

stable’, sabz-āshyāneh ‘green ceiling’, sabz-ayvān 

‘green balcony’, sabz-bādbān ‘green sail’, sabz-bāgh 

‘green garden’, sabz-farsh ‘green carpet’, sabz-golshan 

‘green flower-garden’, sabz-kārgāh ‘green workshop’, 

sabz-khvān ‘green table’, sabz-manzareh ‘green 

panorama’, sabz-maydān ‘green field’ sabz-pol ‘green 

bridge’, sabz-tāq ‘green arch’, sabz-tasht ‘green bowl’, 

and sabz-tā’us ‘green peacock’ are poetic epithets for 

the sky—in addition to similar compounds using the 

words for blue, e.g. lājvardi-saqf ‘lapis lazuli colored 

roof’ or fayruzeh-tasht ‘turquoise bowl’. Moreover, the 

words for green of Arabic origin اخضر akhzar and خضرا 

khazrā are used for epithets of the sky or heaven, such 

as charkh-e akhzar ‘green wheel’. 

Indic 

Hindi distinguishes blue (neela) and green (hara). Words 

for light blue are considered to be a shade of blue. 

Basque 

Historically, the Basque language did not distinguish 

between “blue”, “green”, and “gray”, using the term 

urdin to cover all three. However, present day usage is 

to reserve the word urdin for “blue”, with borrowings 

from Castilian Spanish making up the other two terms 

(berde from Spanish “verde”, green, and gris from 

Spanish “gris”, gray). 

Uralic 

Finnish 

Finnish makes a distinction between vihreä (green) and 

sininen (blue). Turquoise or teal (turkoosi or sinivihreä) 

is considered to be a separate, intermediate, color 

between green and blue, and black (musta) is also 

differentiated from blue. 


The name for color blue, sininen is shared with other 

Finnic languages and is thus dated to the era of the 

Proto-Finnic language (ca. 5000 years old). However, it is 

also shared with the unrelated language Russian (синий, 

siniy), suggesting that it is a loanword. (Alternatively, the 

Russian word синий could represent Finnic substratum). 

The word vihreä (viher-, archaic viheriä, viheriäinen) is 

related to vehreä “verdant” and vihanta “green”, and 

viha “hate”, originally “poison”. It is not shared with 

Estonian, in which it is roheline, probably related with 

the Estonian word rohi “grass”. However, the form viha 

does have correspondences in related languages as far 

as Permic languages, where it means not only poison 

but “bile” or “green or yellow”. It has been originally 

loaned from an Indo-Iranian protolanguage and is 

related to Latin virus “poison”. Furthermore, the word 

musta “black” is also of Finnic origin. 

The differentiation of several colors by hue is at least 

Baltic-Finnic (a major subgroup of Uralic) in origin. 

Before this, only red (punainen) was clearly distinguished 

by hue, with other colors described in terms of 

brightness (valkea vs. musta), using non-color adjectives 

for further specificity. Alternatively, it appears that the 

distinction between valkea and musta was in fact “clean, 

shining” vs. “dirty, murky”. The original meaning of sini 

was possibly either “black/dark” or “green”. Mauno 

Koski’s theory is that such that dark colors of high 

saturation — both blue and green — would be sini, 

while shades of color with low saturation, such as dark 

brown or black, would be musta. Although it is theorized 

that originally vihreä was not a true color name and was 

used to describe plants only, the occurrence of vihreä 

or viha as a name of a color in several related languages 

shows that it was probably polysemic (meaning both 

“green” and “verdant”) already in early Baltic-Finnic. 

However, whatever the case with these theories, 

differentiation of blue and green must be at least as old 

as the Baltic-Finnic languages.


Applying Colour Names to Image Description by Joost van de Weijer and Cordelia Schmid 

Abstract 

Photometric invariance is a desired property for color 

image descriptors. It ensures that the description 

has a certain robustness with respect to scene 

incidental variations such as changes in viewpoint, 

object orientation, and illuminant color. A drawback 

of photometric invariance is that the discriminative 

power of the description reduces while increasing 

the photometric invariance. In this paper, we look 

into the use of color names for the purpose of image 

description. Color names are linguistic labels that 

humans attach to colors. They display a certain 

amount of photometric invariance, and as an additional 

advantage allow the description of the achromatic 

colors, which are undistinguishable in a photometric 

invariant representation. Experiments on an image 

classification task show that color description based 

on color names outperforms description based on 

photometric invariants. 

1. Introduction 

There exists broad agreement that local features are 

an efficient tool for image classification due to their 

robustness with respect to occlusion and geometrical 

transformations [1]. Of the multiple approaches which 

have been proposed to describe the shape of local 

features the SIFT descriptor [2] was found to be among 

the best [3], and is currently the most used shape 

descriptor. Only recently people have started to enrich 

local image descriptors with color information [4, 5, 6]. 

The main challenge for color description is to obtain 

robustness with respect to photometric variations as 

are common in the real world, such as shadow and 

shading variations and changes of the light source 

color. For this purpose color descriptors are generally 

based on photometric invariants [4, 5], such as hue and 

normalized RGB. In increasing the amount of invariance 

one should always consider the loss of discriminative 

power. Photometric invariants are, for instance, blind to 

the achromatic colors, black, grey, and white, because 

from a photometric point of view these could be 

produced from the same patch by varying the intensity. 

It is however questionable if for real-world applications 

full photometric invariance is optimal, and the negative 

effect due to the loss of discriminative power is not too 

high. 

In describing the colors of objects in the real-world 

people make use of color names such as ”red”, ”black” 

and ”olive”. Color names have been primarily studied 

in the fields of visual psychology, anthropology and 

linguistics [7]. Within an image understanding context 

color naming is the action of assigning a linguistic color 

label to image pixels [8, 9], and has been mainly used in 

image retrieval applications [10]. Color names possess 

a certain degree of photometric invariance. In addition 

color names include labels for the achromatic colors: 

”black”, ”grey” and ”white”, which from a photometric 

invariance point of view are impossible to distinguish. 

In this paper, we explore the applicability of color names 

as a basis for color image description. As discussed 

they possess robustness to photometric variations, 

while preserving the discriminative power to distinguish 

achromatic colors. The aim is to find out if the traditional 

choice to use photometric invariants to describe the 

color content, should not be replaced by a new research 

direction, in which partial photometric invariance 

and discriminative power are balanced in a more 

sophisticated way. 

2. Colour Name Descriptor 

The set of color names used in English is vast and 

includes labels such as ”white”, ”green”, ”pastel”, and 

”light blue”. In this paper we use the 11 basic color 

terms of the English language: black, blue, brown, grey, 

green, orange, pink, purple, red, white, and yellow. The 

basic color terms were defined in the influential work on 

color naming of Berlin and Kay [11]. A basic color term 

of a language is defined as being not subsumable to 

other basic color terms (e.g. turquoise can be said to be 

a light greenish blue, and is therefore not a basic color 

term). We define the color descriptor K as the vector 

containing the probability of the color names given an 

image region R

K = {p (n 1 |R) , p (n 2 |R) , ..., p (n 11 |R)} (1) 

with 

p (n i |R) = 1/N ∑/x2R p (n i |f (x) ) (2) 

where n i is the i-th color name, x are the spatial 

coordinates of the N pixels in region R, f = {L*; a*; b*}, 

and p (n i |f ) is the probability of a color name given a 

Fig. 1. Ebay images and hand-segmentations: a ”blue” car, a 

”yellow” dress, ”grey” shoes, and ”brown” pottery. 

pixel value. 

We compute the probabilities p (n i |f ) from a set of 

manually annotated images. Here, we use images 

from the Ebay auction website. On this site users 

describe objects for sell with an explanatory text, 

often containing color names. We have assembled 440 

such images, 40 images per color name, and handsegmented 

the regions corresponding to the color 

label (see Fig. 1). All images are gamma corrected. Next 

histograms of the pixels in the segmentation regions are 

computed in L*a*b* -space with cubic interpolation (we 

use a histogram size of 10x20x20 bins in L*a*b* -space). 

The 40 normalized histograms of each color name are 

averaged to compute p (f |n i ). Subsequently, p (n i |f ) is 

computed with Bayes Law by assuming an equal prior 

over the color names. We experimented with simple 

color constancy algorithms (Grey- World and max-RGB) 

to compensate for the illuminant color, but found that 

they did not improve classification result. 

We proposed an alternative approach to compute the 

color name distributions in [12]. There the distributions 


are automatically learned from weakly labelled images 

which are retrieved with Google image search. 

3. Balancing Photometric Invariance Versus 

Discriminative Power 

In the introduction, we questioned whether in balancing 

photometric invariance versus discriminative power, 

existing color descriptors do not focus too much on 

photometric invariance. In Table 1 the occurrence of 

color names in about 40.000 images of the Corel Data 

set is given. The color name distribution is computed by 

appointing each pixel to its most probable color name, 

with arg max ni p (n i |f (x) ). Striking is the abundance of 

the achromatic colors: 44% of the pixels is appointed 

to an achromatic color name. The dark color ”brown” is 

most common and only ”blue” and ”green” can be said 

to be frequent bright colors. For photometric invariant 

black blue brown grey green orange pink purple red white yellow 

19 12 23 19 10 2 2 2 4 6 1 

Table 1. Percentage of pixels assigned to color name in Corel 

data set. 

Fig. 2. Example of color name assignment on toy-image. The 

color names are represented by their corresponding color, e.g. 

the pixels on the ball are assigned to red, pink and brown. 

Note that color names display a certain amount of photometric 

robustness. 

representations achromatic colors are undefined, and 

consequently remain undescribed by the descriptors, 

resulting in a considerable loss of discriminative power.


On the other hand, a certain degree of photometric 

invariance is desired. It avoids that many instances of 

the object, under various viewing angles and lighting 

conditions, have to be represented in the training set. 

As an alternative to photometric invariants we propose 

to use color names, which exhibit robustness with 

respect to photometric variations, while remaining 

capable to distinguish between the achromatic colors. 

In Fig. 2 an illustration of the photometric robustness 

of color names is shown: the most likely color name 

for each pixel is depicted. Most objects are given a 

single color name independent of shading and specular 

effects, e.g. the blocks in the upper left quarter 

are described by a single color name. However the 

photometric robustness is limited: multiple color names 

are assigned to the yellow block and the red ball in the 

left bottom quarter. A full photometric invariant such as 

hue would consider all pixels of the yellow block and red 

ball as belonging to the same color. In the experimental 

Scale and Affine Invariant Feature Detection 

Shape Normalization 

Feature Description 

Shape 

Fig. 3 Overview of bag-of-words approach. In the feature 

detection phase a set ofi nformative points at various scales 

are detected. Each feature is subsequently described by its 

shape and color information in the feature description phase. 

section we investigate if the loss in photometric 

invariance in going from full photometric invariants to 

color names is compensated by a gain in discriminative 

power. 

4. Bag-of-Words Approach to Image Classification 

In the experimental phase the color name descriptor is 

tested for image classification. In image classification 

SIFT 

+ 

Color 

Color Names 

the task is to predict the presence of an object in a 

test image. The bag-ofwords approach is generally 

considered the most successful approach to image 

classification [13]. The approach has been motivated on 

the bag-of-words approach to document analysis. In the 

case of image classification the words are replaced by 

visual words representing frequent structures in images 

such as blobs, corners, T-junctions, etc. The method 

starts by detecting a set of image regions (we apply 

a Harris-Laplace detector [1]) and their corresponding 

scales in an image (see Fig. 3). Next, in the description 

phase, all patches are normalized to a standard size and 

a descriptor is computed for all regions. The descriptors 

are clustered by a K-means algorithm to form the set of 

visual words. Subsequently, each image is represented 

by a frequency histogram of the visual words. Based 

on these histograms a classifier is trained for all classes 

(we apply a linear SVM). A test image is classified with 

all classifiers, and is appointed to the class for which it 

obtained the highest SVM score. 

The majority of the bag-of-words methods are based 

on shape alone, and ignore color information. Van de 

Weijer and Schmid [4] extended the model to also 

include color information. Their color description is 

based on histograms of photometric invariants. In this 

paper, we aim to improve the discriminative power of 

the color description. For this purpose we base the 

color description on color names instead of photometric 

data set soccer flowers 

method color shape color & shape color shape color & shape 

HSV-SIFT - - 77 - - 78 

hue 75 84 40 79 

opponent 75 58 85 39 65 79 

color names 86 89 57 81 

Table 2. Classification rates on soccer and flower data set 

for hue, opponent color derivative, HSV-sift and color names. 

The results are given for only color, only shape (SIFT) and 

the combination of color and shape.

invariants. The combined shape and color descriptor is 

computed as follows. For the shape description, S, we 

use the SIFT descriptor [2] which is concatenated to the 

color name descriptor K according to: 

B = (^F, ^K) (3) 

where ^: indicates that the vector is normalized. The 

visual words are learned in this combined shape-color 

space and can be thought to contain red corners on a 

black background, blue blobs on a yellow background, 

etc. 

5. Results 

We compare the color name descriptor to the following 

descriptors from literature: the hue descriptor and 

the opponent color derivative descriptor proposed in 

[4], and the HSV-SIFT descriptor proposed in [6]. The 

hue and opponent derivative descriptor describe the 

patch with a color histogram of 36 bins. The HSV-SIFT 

descriptor is constructed by subsequently applying the 

SIFT descriptor to the H, S, V channel after which the 

three SIFT descriptors, of length 128, are combined to 

form one descriptor of length 384. The descriptors are 

tested on two online available1 data sets: the soccer 

team set [4] and the flower data set [14]. 

5.1. Soccer Teams Classification 

The soccer data set consists of seven classes with 

25 training images and 15 test images per class. The 

results for only color, only shape and combined color 

and shape are given in Table 2. The use of color for 

this classification problem is crucial: the SIFT shape 

description obtains a performance of 58%. Most striking 

are the good results in the case of only color; the 

color name descriptor improves performance by 10% 

compared to the hue and opponent descriptor. In a 

combination with shape the gain is still 5%. The HSV- 

SIFT descriptor obtains unsatisfactory results for this 

data set. 

The capability to distinguish between the achromatic 

colors plays an important role in the classification of this 

soccer team set, e.g. the outfits of AC Milan are blackred, 

of PSV black-white and of Liverpool red (see Fig.4). 


The invariant description looses too much discriminative 

power, as can be seen from the confusion matrix Table. 

3. The color name descriptors’s capacity to distinguish 

black from white in combination with its photometric 

robustness proved fruitful. 

5.2. Flower Classification 

The flower data set consists of 17 classes varying in 

color, texture, and shape [14]. Of each class 40 train 

hue R 

R-BL 

R-W 

R 67 20 13 

W 

R-BL 13 73 7 7 

R-W 13 13 73 

W-BL 

W 73 27 

B 

B-P 

W-BL 20 73 7 

B 7 73 20 

B-P 7 93 

CN R 

R 100 

R-BL 

R-W 

W 

R-BL 93 7 

W-BL 

B 

B-P 

R-W 13 7 67 7 7 

W 87 13 

W-BL 7 7 87 

B 7 7 67 20 

B-P 100 

Table 3. Confusion Matrices for soccer data. Left: hue based 

color descriptor. Right: color name based descriptor. The 

soccer teams are abbreviated with the colors of their outfit: 

Liverpool (Red), AC Milan (Red-BLack), PSV (Red-White), 

Madrid (White), Juventus (White-BLack), Chelsea (Blue), 

Barcelona (Blue-Purple). Note the drop in black and white 

related confusions as indicated by the underlined numbers. 

and 40 test images exist. The results are summarized 

in Table 2. For color alone, the color name descriptor 

obtains significantly better results than the full 

photometric descriptors. For the combined color and 

shape description, the performance of the color name 

descriptor is still slightly better than the other methods. 

6. Conclusions and Discussion 

The research effort to enrich local image description 

with color information has been primarily focused on 

appending photometric invariant information to shape 

descriptors. In this paper, we show that full photometric 

invariance is not optimal due to the loss of discriminative 

power. The proposed descriptor based on color names 

outperforms the photometric invariant representations. 

This indicates that the loss of photometric invariance 

in going from photometric invariants to color names


is more than compensated by a gain in discriminative 

power. However, we do not believe color names to 

provide the optimal balance between the two, and 

from this perspective we see this work as a motivation 

to further investigate color descriptors with only partial 

photometric invariance. Nevertheless, results show 

that the proposed descriptor significantly outperforms 

existing descriptors for description based on color 

alone, and does moderately improve description based 

on color and shape. 

Fig. 4. Example images of the soccer team and flower data 

base. First row: AC Milan, Liverpool, PSV, and Juventus. 

Second row: daffodil, snowdrop, bluebell, and crocus. 

7. References 

[1] K. Mikolajczyk and C. Schmid, “Scale and affine 

invariant interest point detectors,” International Journal 

of Computer Vision, vol. 60, no. 1, pp. 62–86, 2004. 

[2] D.G. Lowe, “Distinctive image features from scaleinvariant 

keypoints,” International Journal of Computer 

Vision, vol. 60, no. 2, pp. 91–110, 2004. 

[3] K. Mikolajczyk and C. Schmid, “A performance 

evaluation of local descriptors,” IEEE Transactions on 

Pattern Analysis and Machine Intelligence, vol. 27, no. 

10, pp. 1615–1630, 2005. 

[4] J. van de Weijer and C. Schmid, “Coloring local 

feature extraction,” in Proc. of the European Conference 

on Computer Vision, Graz, Austria, 2006, vol. 2, pp. 

334–348. 

[5] J.M. Geusebroek, “Compact object descriptors from 

local colour invariant histograms,” in British Machine 

Vision Conference, 2006. 

[6] A. Bosch, A. Zisserman, and X. Munoz, “Scene 

classification via pLSA,” in ProcP of the European 

Conference on Computer Vision, 2006. 

[7] C.L. Hardin and L. Maffi, Eds., Color Categories in 

Thought and Language, Cambridge University Press, 

1997. 

[8] A. Mojsilovic, “A computational model for color 

naming and describing color composition of images,” 

IEEE Transactions on Image Processing, vol. 14, no. 5, 

pp. 690–699, 2005. 

[9] R. Benavente, M. Vanrell, and R. Bladrich, “A data 

set for fuzzy colour naming,” COLOR research and 

application, vol. 31, no. 1, pp. 48–56, 2006. 

[10] Y. Liu, D. Zhang, G. Lu, and W-Y. Ma, “Region-based 

image retrieval with high-level semantic color names,” in 

Proc. 11th Int. Conf. on Multimedia Modelling, 2005. 

[11] B. Berlin and P. Kay, Basic color terms: their 

universality and evolution, Berkeley: University of 

California, 1969. 

[12] J. van de Weijer, C. Schmid, and J. Verbeek, 

“Learning color names from real-world images,” in 

Proc. of the Computer Vision and Pattern Recognition, 

Minneapolis, Minnesota, USA, 2007. 

[13] J. Willamowski, D. Arregui, G. Csurka, C. R. Dance, 

and L. Fan, “Categorizing nine visual classes using local 

appearance descriptors,” in IWLAVS, 2004. 

[14] M-E. Nilsback and A. Zisserman, “A visual 

vocabulary for flower classification,” in IEEE Conference 

on Computer Vision and Pattern Recognition, 2006. 

Reference: VAN DE WEIJER, J. & SCHMID, C., nd. Applying Colour Names to Image Description. [online] Available at: [Accessed 28/04/11].


Color Names: More Universal Than You Might Think – ScienceDaily 

From Abidji to English to Zapoteco, the perception and 

naming of color is remarkably consistent in the world’s 

languages. 

Across cultures, people tend to classify hundreds of 

different chromatic colors into eight distinct categories: 

red, green, yellow-or-orange, blue, purple, brown, pink 

and grue (green-or-blue), say researchers in this week’s 

online early edition of the Proceedings of the National 

Academy of Sciences. 

Some languages classify colors into fewer categories, 

but even these categories are composites of those 

eight listed above, said Delwin Lindsey, the study’s lead 

author and an associate professor of psychology at Ohio 

State University. 

“Though culture can influence how people name colors, 

inside our brains we’re pretty much seeing the world 

in the same way,” he said. “It doesn’t matter if you’re 

a native of Ivory Coast who speaks Abidji or a Mexican 

who speaks Zapoteco.” 

He conducted the study with Angela Brown, an 

associate professor of optometry at Ohio State. 

Lindsey and Brown used data from the World Color 

Survey, a collection of color names supplied by 2,616 

people of 110 mostly unwritten languages spoken by 

mostly preindustrial societies. The survey’s 320 different 

colors are organized into eight rows of 40 color chips 

per row (black, white and grays are each in their own 

category.) 

The researchers used the survey because it included 

many people from preindustrial societies whose color 

names are thought to be relatively uncontaminated by 

contact with highly industrialized cultures whose color 

names closely resemble those found in English. 

Lindsey and Brown devised a statistical method that 

let them determine the optimum number of color 

categories based on the color terms uncovered in the 

study. 

“My own intuition was that if we looked across the world 

at different languages, people would obviously use 

different names, but roughly we’d find maybe 11 names 

used to partition color space,” Lindsey said. “That’s not 

at all the case. 

“By looking at more traditional cultures, we found 

that many have fewer color names, yet these names 

correspond to colors that English-speaking cultures also 

discriminate linguistically,” he continued. 

Using a technique called cluster analysis, he and Brown 

analyzed data gathered by previous color survey 

researchers. This approach helped them measure the 

similarity across all the different cultures in terms of how 

each applies name to color. 

“We have names for 11 basic colors in English,” Lindsey 

said. “Some cultures have two, some have three. We 

wanted to know if the cultures that say they only have 

two color terms chose colors similar to those selected 

by cultures that have more color names.” 

They found that colors fall into eight distinct categories. 

“Across cultures the average color-naming patterns of 

the clusters all glossed easily into single or composite 

English patterns,” Lindsey said. 

“Even though people are really diverse, when push 

comes to shove, they are incredibly English-like,” he 

said. “Many cultures don’t have all of the English color 

categories, but they have many of them. And the ones 

that aren’t exactly English turn out to be what we call 

composites – simple combinations of adjacent color 

categories.” 

That, says Lindsey, helps explain categories like grue 

(green-or-blue) and yellow-or-orange. 

The researchers found a major distinction between 

warm and cool categories for many of those cultures 

that have just two or three common colors. That

distinction tended to coincide with English colors that 

are thought to be warm (yellows, reds and oranges) and 

cool (greens and blues.) 

“While there is some diversity in the location of the 

color boundaries, there is an absolutely rock solid 

boundary across all the cultures, which English speakers 

would call warm and cool,” Lindsey said. 

For example, some societies lump all the cool colors 

into one category, and all the warm colors into another 

category, while other societies subdivide warm and 

cool colors into several categories. In the case of the 

subdivided categories, there still exist color boundaries 

that separate warm from cool. 

Lindsey said the next stage in this research is to look 

at physiology of color perception, as some researchers 

believe that infants have the innate ability to recognize 

certain colors. 


Reference: Anon, 2006. Color Names: More Universal Than You Might Think. ScienceDaily. [online] Available at:

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