Cognitive Aspects of Color

IntroductionPrevious Work

Our Results & ContributionSummary

Cognitive Aspects of ColorAspetti cognitivi del colore

G. Beretta N. Moroney

Print Production Automation LabHewlett-Packard Laboratories

Palo Alto, California

IVa¯ Conferenza Nazionale del Gruppo del Colore

Como, 18 settembre 2008

Beretta, Moroney Cognitive Aspects of Color



Outline

1 IntroductionNew Challenges in Color ManagementLexical ColorColor Naming

2 Previous WorkAttempts to Compile ThesauriExtensions of the Basic Color TermsSummary

3 Our Results & ContributionColor Naming on the WebTools Leveraging the CorpusWork in ProgressResults So Far




Broad Problem DescriptionSpecific Problem DescriptionCategorical Perception

Outline








Emerging Display Technologies





Today’s Nomadic Road Warrior Works Wherever





Color Management Is Just Expected to Work





Color Integrity

At the 1997 panel discussion on color fidelity vs. color integrityat the Color Imaging Conference in Scottsdale we had argued:

Color fidelity cannot be achieved in consumer applicationslike Internet shoppingA color never comes alone: it is part of a paletteColor fidelity is not necessary if color integrity ismaintained

1 Foveal colors should not cross name boundaries2 The error vectors should have a uniform flux

Distortions are unavoidable, we need to control them





Naming of Colors

In real life, the names of colors are often less important thanthe names of colors of objects

ExampleDelk & Fillenbaum experiment (1965)





More Applications of Color Naming

Once we have facilities for processing color by name, we canfind more applications:

Better user experience in GUIsAutomatic nudging of text and logo colors for readibility invariable data printing

Gamut mapping for HDR and wide gamut displaysCulture-independent preferred color renderingThematic rendering





Outline








Metric Color Discrimination





Color Communication

amber

stimulus detectors early mechanisms pictorial register

context parameters

Color lexicon

color nameaction

edgescontourmotiondepth

color

…

internallightness

hue

chromaetc.

lightness

hue

chromaetc.

apparent colorrepresentation

color space





Color Naming Constraints

1 Physiological basis of color perception2 General color cognition

basic vs. derived color categoriesrole of prototypesformatting of internal representation

3 Color communicationsharable knowledge about the worldmetaphorical namessemantic and syntactic constraints

Result: an observer can discriminate more efficiently between apair of colors straddling a category boundary than between apair in the same category





Categorical Color Discrimination





Outline








Development of Color Naming

Color naming is acquired, not geneticsocio-economic status (SES)

Occurs late in child’s development, but age is decreasingwith increase of technology

1900: basic four colors @ 8 years1950: @ 5 years of age





Color Ontogeny of Languages

Brent Berlin and Paul Kay, University of Berkeley, 1969The physiology underlying even the unique hues isunknownThere is no natural categorization

yellow

red

greenyellow

green

blue brownwhiteandblack

orangeand/orpinkand/orpurpleand/orgray

I II III IV VI VIIV





The Structure of Color Naming Spaces

1 What is the set of color names?2 Where are the boundaries for synonyms?

Without a natural categorization, it is not clear where theboundary for synonyms areAn important problem in avionics

3 How are the color names categorized algorithmically?

.55

.45

.35

.25

.15.05 .15 .25 .35 .45

v’

u’

David L. Post, 1988

green

white yelloworange

red

peachaqua

graypink

bluepurple

1. 1. 1.

1..98 .92 .65 .53

.99 .98 .97 .88 .62.97 .91 .94 .84 .66 .5.71 .71 .68 .7 .44

.33

.73 87.91.87 .53.71.59.73.74.57.33.46.44.31

.75.88.96.98.58.54.63.59 .5

.39

.45

.49.63.78.81.63.47.33.35.56.61.61.52

.98.96.93.52

.51

.61.53.53 .47.47

.47.52.41.45

.64

.56 .43.56

.56

.53

.48 .3

.57.56.44

.77.63.38

.82.65.36

.69.42

.39

.45.45

.47.48.28

.37.38

.36

.35

.53.64 .5

.62.75 .53

.74.82.53

.9.84 .48.92.89.53

.98 .9.52

.98 .9 .53

.97 .9

.96.91 .6

.97.92.63

.97.94

.97

.32.46.56.68.76 .74.75.58 .35.53 .7 .8 .85.87.82.74.52.74.81.87.91.89.86

.33.57.69.83.92.88.87.57 .7 .82.86.88.56.68.72.74

.51.63

.5

.48.44

.56.56.47.54.67.77.59.48.7 .83 .8 .72.65.82.92 .9 .75.69.83.93.89.81.83.94.92

.52.91.95

.84B = CIE Standard Illuminant B

E = equal-energy pointD = CIE Standard Illuminant D65C = CIE Standard Illuminant C

A = CIE Standard Illuminant A

A

BED

C





What is A Color Thesaurus?

DefinitionA thesaurus is a compilation of synonyms (and antonyms) withetymological and semantical information as well as examples todisambiguate the synonyms

There is a number of dictionaries of color namesIn 1955 Kelly and Judd produced an early color thesaurusIn 2007 Nathan Moroney created an online thesaurus ofcolor names

based on an earlier online color naming experiment




Attempts to Compile ThesauriExtensions of the Basic Color TermsSummary

Outline








The ISCC–NBS Method of Designating Colors and ADictionary of Color Names

Kenneth L. Kelly & Deane B. Judd, 1955Names from over a dozen compilations in use in the USA;uniformity in Munsell space1933 recommendations by I.H. Godlove + scheme of huemodifiers + heuristics

Munsell Value

Munsell C

hroma

black dark gray medium gray light gray white

–ish black dark –ish gray –ish gray light –ish gray –ish white

blackish darkgrayish grayish

paleor

light grayishvery pale

very dark dark moderate light very light

very deep deep strong brilliant

vivid





Coloroid Color NamesAntal Nemcsics, 1993

Historical pigment names; process and structure notdocumented; 70’000 observersStructure hierarchy: 7 domains, 76 primary colorsSeveral errors and inconsistencies

0 10 20 30 40 50 60 70 80 90 1000

10

20

30

40

50

60

70

80

90

100

T

V

A = 20

Anatolian brown

cement greybroken warm white

Roman ochre

brown beige

Arsigont

Pompeian yellow

orange ochreIndian orange





Outline








Color Names for AvionicsDavid L. Post and collaborators, 1985–1989

Start with 12-name vocabulary (basic terms + peach)Psychophysics; present 210 stimuli (uniform in UCS) onvarious backgrounds under various illuminantsBoundaries enclose areas within which the modalcolor-name response corresponds with the color nameProbability of obtaining the modal color-name response

.55

.45

.35

.25

.15.05 .15 .25 .35 .45

v’

u’

David L. Post, 1988

green

white yelloworange

red

peachaqua

graypink

bluepurple

1. 1. 1.

1..98 .92 .65 .53

.99 .98 .97 .88 .62.97 .91 .94 .84 .66 .5.71 .71 .68 .7 .44

.33

.73 87.91.87 .53.71.59.73.74.57.33.46.44.31

.75.88.96.98.58.54.63.59 .5

.39

.45

.49.63.78.81.63.47.33.35.56.61.61.52

.98.96.93.52

.51

.61.53.53 .47.47

.47.52.41.45

.64

.56 .43.56

.56

.53

.48 .3

.57.56.44

.77.63.38

.82.65.36

.69.42

.39

.45.45

.47.48.28

.37.38

.36

.35

.53.64 .5

.62.75 .53

.74.82.53

.9.84 .48.92.89.53

.98 .9.52

.98 .9 .53

.97 .9

.96.91 .6

.97.92.63

.97.94

.97

.32.46.56.68.76 .74.75.58 .35.53 .7 .8 .85.87.82.74.52.74.81.87.91.89.86

.33.57.69.83.92.88.87.57 .7 .82.86.88.56.68.72.74

.51.63

.5

.48.44

.56.56.47.54.67.77.59.48.7 .83 .8 .72.65.82.92 .9 .75.69.83.93.89.81.83.94.92

.52.91.95

.84B = CIE Standard Illuminant B

E = equal-energy pointD = CIE Standard Illuminant D65C = CIE Standard Illuminant C

A = CIE Standard Illuminant A

A

BED

C





Influence of Culture on Color NamingHeinrich Zollinger, ca. 1975

What is the link between the neurobiology of color visionand color naming (embodyment)?Subjects native speakers in:

chemistry students in German, French, English, Hebrew,Japaneseart students in German, HebrewJapanese childrenanalphabets in Kekchi, Misquito

Tasks:1 list minimally necessary color names2 list supplementary color names3 total must be 124 name 117 Munsell chips





Frequency of OccurrenceChemistry students

60%

20%

40%

Häufigkeit

5R 5RP5P5PB5B5BG5G5GY5Y5YR

rotgelb

orange

braun

grünblau violett

rot

rosa

purpur

ETH-Zürich

60%

20%

40%

かいすう

5R 5RP5P5PB5B5BG5G5GY5Y5YR

80%

オレンジ

TKD-東京

朱あか黄き

茶ちゃ

橙ダイダイ

緑みどり青あお

水色

みずいろ

紫むらさき

ピンク

桃色

ももいろ





Robotic Agent Naming ColorsJohan Lammens, 1994





Outline








Number of Color Categories

Berlin & Kay: 11 basic termswhite, black, red green yellow, blue, brown, orange, pink,purple, grey

ISCC–NBS: 267 categoriesNemcsics: 76 primary colorsBoynton & Olson: 15 nonbasic terms are frequently used

tan, peach, olive, lavender, violet, lime, salmon, indigo,cyan, cream, magenta, turquoise, chartreuse, rust, maroon11 + 15 = 262 uninformed subjects were presented twice the 424 OSApatches and asked how many colors they sawone subject estimated 30, the other 80

11 is too low a number to categorize colors





Limitations of These Solutions

Only ISCC–NBS has a bona fide thesaurus266 color categories with annotated synonyms

Even with 7,500 color names, the dictionary is very limiteda snapshot in time (1955)mostly government and industry relatedonly in English

It is not clear how many categories there areThese limitations make it less useful




Color Naming on the WebTools Leveraging the CorpusWork in ProgressResults So Far

Outline








Research Constraints

We are interested in the names of color patchesNot in free lists of color namesNot in color object namesNot in the evolution of color names

carta zucchero102, 140, 204; #668ccc; very rare.azzurro, indacoazzurro scuro, ciano.ant. maroone chiaro, ocra.

celeste54, 176, 239; #36b0ef; rare.ciano, azzurrocyan, carta zucchero.ant. arancio, arancione.

yellow

red

greenyellow

green

blue brownwhiteandblack

orangeand/orpinkand/orpurpleand/orgray

I II III IV VI VIIV





Goal

1 Large dictionaryextensive, through crowd-sourcingevolves through timenot limited to one language

2 Number of synonym categories� 12decided though crowd-sourcingnot 266 like in ISCC–NBS thesaurus. . . or 26, or 30, or 80. . .

3 Algorithm for determining categoriesexplicitly ask user for a specific and a general nameconstruct separate categorizations for eachexplore boundary-finding algorithms





Color Naming Experiment





Outline








The Color Thesaurus in English

The ephemerality is built in!

http://www.hpl.hp.com/personal/Nathan_Moroney/color-thesaurus.html







Color Zeitgeist

Can easily derive secondary toolsTag cloud visualization of the color name queries

Example





Italian Color Thesaurus





Qualifying the Corpus

Problems:we observed about5% disruptiveparticipants in theexperimentvariability of rarelyused names

Solution is to collectexplicit feedback on theglobal statistics fromeach participantMore efficient thanrecruiting domainspecialists





Expanding the Corpus

Problem: Insufficient data in non-English corporaSolutions:

1 brute force: adding a hundred names require tens ofthousands of participants

because of redundancy (long tail distribution) this is veryinefficientwhen a name is missing, the cost for completing the corpusis high

2 targeted harvesting: get participants to find sparseregions. . .

3 . . . and submit relevant data





Contributed Name Distribution

0

400

800

1200

1600

2000

brickemerald

leaf green

eggplantchocolate

cornflower

blue gray

fluorescent green

kelly green

burnt orange

khakibright blue

beigerosepeach

grass green

sea green

periwinkle

lilacmaroonlight blue

turquoiseyellow

magentaredgreen

greenbluepurplepinkredblacklime greenbrownmagentavioletsky blueorangeyellowteallight greenfuchsiaturquoiseaquaroyal blueforest

light bluelavendergraynavy bluemaroonlimedark bluedark greenlilacoliveolive greencyanperiwinklemint greenbright greenmauvesea greenhot pinkneon greenseafoam

grass greentanyellow greennavypeachburgundysalmonlight purplerosegoldplumbrick redbeigemustardwhiteindigobright bluechartreuselight brownaquamarine

khakidark browndark purplemoss greenburnt orangespring greenpea greenbaby bluekelly greendark pinkrustblue greenfluorescent greensagehunter greenpale greenblue graycobaltmidnight bluelight pink

cornflowercreamred orangedark redchocolatecrimsoncoralapple greeneggplantgoldenrodmedium blueocean blueleaf greenbright purplegrapelight yellowemeraldjadeochrearmy greenbrick





Expanding the Corpus





Outline








Limitations of the First Experiments

We have solved:how can we screen out bogus data?how do we get scalability?how can we make the experiments more collaborative?

Remaining problem:the categorization lacks fixed boundariessynonyms are formed ad hoc by searching for the colornames with the smallest CIECAM02 color differencegreater than 5∆E∗

02,C





Planned Extensions

Algorithm for determining categoriesexplicitly ask user for a specific and a general nameconstruct separate categorizations for eachexplore boundary-finding algorithms





The Density of Color Names

g

j

green

yellow

orange

brown

pinkred

blue

purple

whitegrey





Outline








Statistics

120’760 synonyms served from September 2007 throughAugust 2008Syndicated by

Core Design 77http://www.core77.com/blog/object_culture/online_color_thesaurus_7966.asp

Aubrey Jafferhttp://people.csail.mit.edu/jaffer/Color/Dictionaries

MagCloud reference magazine, available fromhttp://magcloud.com/browse/Issue/2344


http://www.core77.com/blog/object_culture/online_color_thesaurus_7966.asp

http://people.csail.mit.edu/jaffer/Color/Dictionaries

http://magcloud.com/browse/Issue/2344



Summary

There are applications requiring a color thesaurusCurrent solutions are not adequateWe have a promising early solution that scales well

OutlookResearch is required to tile the color space and build a truethesaurusEventually, the tool should be so robust we can run it for alanguage none of us speaks




Acknowledgments

We are indebted to Prof. Lucia R. Ronchi of the FondazioneGiorgio Ronchi in Florence for many fruitful discussions and forcompiling the book on Color and Language




Questions and Discussion

mailto:[email protected]://www.hp.com/blogs/mostly_colorhttp://www.hpl.hp.com/personal/Giordano_Beretta/


Technology

Cognitive Aspects of Color