towards robust categorical colour perception

Towards robust categorical colour perception

G. Beretta N. Moroney J. Recker

Print Production Automation LabHewlett-Packard Laboratories

Palo Alto, California

11th Congress of the International Colour AssociationSydney, 27 September – 2 October 2009

Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 1 / 31

Outline

1 Problems

2 Global vs. local colour differences

3 What and where are the categories?

4 New paradigm: use crowd-sourcing

5 Status & conclusions

Outline

1 Problems

Describing colours

HTML colour specification

#007CB0#EF4123

#848688

#BF1E74

#F89F6D

#008F4C

#F499B8

#007CB0

Automatic layout in variable data printing

automaticlayout

HP chicletnew palette

Buy HPworkstation xw 8600

HP chicletold palette

Buy HPcomputers

The wide xvYCC gamut

235254

16 240 254

Cb, Cr1

65.0+5.0+5.0-

-0.57 Black

Over White

0 < R’,G’,B’ < 1

1< R’,G’,B’

R’,G’,B’< 0

(Gamut of BT.709-5)

Gamut of xvYCC

Extended

Extended Region

R’,G’,B’< 0

1< R’,G’,B’

Extended

BT.709-5(sRGB)

Chroma

(sRGB)

Applications of colour naming

Better user experience in GUIsAutomatic nudging of text and logo colours for readability invariable data printingGamut mapping for HDR and wide gamut displays

Culture-independent preferred color renderingThematic rendering

Outline

1 Problems

Local colour differences

Stiles Line ElementEllipses plotted 3 x

0 0.2 0.4 0.6

460470

520530

610620

Categorisation

Categorical perception

Definition (Stevan Harnad)A categorical perception effect occurs when

1 a set of stimuli ranging along a physical continuum is given onelabel on one side of a category boundary and another label on theother side and

2 the subject can discriminate smaller physical differences betweenpairs of stimuli that straddle boundary than between pairs that areentirely within one category or the other

.15.05 .15 .25 .35 .45

David L. Post, 1988

white yelloworange

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

.73 87.91.87 .53.71.59.73.74.57.33.46.44.31

.75.88.96.98.58.54.63.59 .5

.49.63.78.81.63.47.33.35.56.61.61.52

.98.96.93.52

.61.53.53 .47.47

.47.52.41.45

.56 .43.56

.48 .3

.57.56.44

.77.63.38

.82.65.36

.69.42

.45.45

.47.48.28

.37.38

.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

.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

.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

.15.05 .15 .25 .35 .45

David L. Post, 1988

white yelloworange

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

.73 87.91.87 .53.71.59.73.74.57.33.46.44.31

.75.88.96.98.58.54.63.59 .5

.49.63.78.81.63.47.33.35.56.61.61.52

.98.96.93.52

.61.53.53 .47.47

.47.52.41.45

.56 .43.56

.48 .3

.57.56.44

.77.63.38

.82.65.36

.69.42

.45.45

.47.48.28

.37.38

.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

.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

.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

Global colour differences — how to find them?

0 10 20 30 40 50 60 70 80 90 1000

A = 20

Anatolian brown

cement greybroken warm white

Roman ochre

brown beige

Arsigont

Pompeian yellow

orange ochreIndian orange

Outline

1 Problems

Colour ontogeny of languages

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

yellow

greenyellow

blue brownwhiteandblack

orangeand/orpinkand/orpurpleand/orgray

I II III IV VI VIIV

Development of colour naming

Colour naming is acquired, not geneticsocio-economic status (SES)Franklin et al., PNAS 105(9): 3221–3225, 2008

adultswithin-categorybetween-category

visual fieldleft right

initia

tion t

infantswithin-categorybetween-category

visual fieldleft right

initia

tion t

Occurs late in child’s development, but age is decreasing withincrease of technology

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

Outline

1 Problems

1 Large dictionarycurrently harvesting unconstrained namesextensive, 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 categoriesconstruct separate categorisations for each colour patchexplicitly ask user for a specific and a general nameexplore boundary-finding algorithms

Multilingual colour naming experiment

http://www.hpl.hp.com/personal/Nathan_Moroney/mlcn.html

The colour thesaurus

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

From colour naming to thesaurus

inverse YCiCii

neighbors

CIECAM02 neighbors

core vocabulary

frequency analysis

scrubbed corpus

substring statistics

merging

spell-checker

exclusionary corpus

conventional usage

substitutionsdeletionstypographic

harmonizationraw corpus

naming experiment

synonyms

antonyms

threshold number unique IP addresses

for each name

Contributed name distribution

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

Handling missing names

corpus scrubbingraw corpusnaming

experiment

synonyms and antonyms

lexical analysis

name found

name harvesting

Expanding the corpus

Robustness: qualifying the corpus

Problems:we observed about3% disruptiveparticipants in theexperimentvariability of rarelyused names

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

Feedback distribution

correct, spot on

neutral

wrong, completely wrong

Outline

1 Problems

What we have so far

Framework for collecting colour names in multiple languagesRobust: colour thesaurus served 194’369 color names as ofWednesday the 23rd of September 2009Robust: feedback mechanism for improving the corpus qualitywith useMechanism to harvest less common namesStill cheating on categorisation: synthetic synonyms vs.categories, but making progress . . .

Experimenting with linguistic analysis tools to find categoryboundaries

No user interface yet to collect category names and antonyms

Is the category hierarchy just 2 deep?

Coloroid color

yellow

orange

yellowish orange 1

broken warm white

cement grey

Anatolian brown

Roman ochre

brown beige

Arsigont

Pompeian yellow

orange ochre

Indian ochre

yellowish orange 2

orange 1

orange 2

orange 3

reddish orange 1

reddish orange 2

violet

green1

green2

7 domains 48 basics 369 names +79 synonyms

Dendrogram — good things to come . . .0

ticglos

ssticky

yultra

coated

seoff.white

semi.glos

light.w

leivory

chalky

texture

lmetallic

silver

parchm

ematte

r white art

surfac

y.weigh

bright

see: Moroney & Beretta,“Nominal scaling of print substrates,” CIC 17,Albuquerque, November 2009

Questions and Discussion

http://www.hpl.hp.com/personal/Giordano_Beretta/http://www.hpl.hp.com/personal/Nathan_Moroney/http://www.hpl.hp.com/people/john_recker/blog: http://mostlycolor.ch

towards robust categorical colour perception

colour naming

hp labs

conclusions

html beretta

beretta

moroney

paradigm

www

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