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

16

235254

16 240 254

Y

Cb, Cr1

65.0+5.0+5.0-

128

-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

Extended Region

R’,G’,B’< 0

1< R’,G’,B’

Extended

BT.709-5(sRGB)

sYCC

xvYCC

0.0

1.0

Luma

Chroma

(sRGB)

1


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

y

x

0.8

0.2

0.4

0.6

0 0.2 0.4 0.6

700

450

460470

480

490

500

510

520530

540

550

560

570

580

590

600

610620

630


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

.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.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


Global colour differences — how to find them?

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

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

red

greenyellow

green

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

ime

[ms]

550

250

350

450

infantswithin-categorybetween-category

visual fieldleft right

initia

tion t

ime

[ms]

900

500

600

700

800

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






Goal

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


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

The colour thesaurus

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


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

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


Handling missing names

corpus scrubbingraw corpusnaming

experiment

synonyms and antonyms

lexical analysis

name found

name harvesting

TRUE

FALSE

crowd


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

good

neutral

poor

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

red

violet

blue

green1

green2

7 domains 48 basics 369 names +79 synonyms


Dendrogram — good things to come . . .0

12

34

Heigh

t

shiny

plas

ticglos

ssticky

med

ium.w

eigh

tmed

ium

photo

wax

yultra

flat

coated

viny

lsm

ooth

dull

diffu

seoff.white

semi

semi.glos

stran

spar

ent

clea

rthin

light.w

eigh

tpa

leivory

yello

wcrea

mbe

ige

soft

eggs

hell

chalky

blue

gree

ngr

aysa

tinde

cora

tive

grain

texture

roug

hbr

own

pear

lmetallic

silver

high

parchm

ent

tan

offic

ematte

pape

r white art

rigid

surfac

eco

lor

canv

aslin

enfin

epa

ttern

heav

y.weigh

the

avy

card

thick

stiff

bright

gold

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


Technology

Towards robust categorical colour perception