Presentation given at the 11th Congress of the International Colour Association, Sydney, 27 September – 2 October 2009

1. Towards robust categorical colour perception
G. Beretta N. Moroney J. Recker
Print Production Automation Lab
Hewlett-Packard Laboratories
Palo Alto, California
11th Congress of the International Colour Association
Sydney, 27 September – 2 October 2009
Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 1 / 31

2. 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
Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 2 / 31

3. 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
Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 3 / 31

4. Describing colours
Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 4 / 31

5. HTML colour specification
#EF4123 #007CB0
#848688
#BF1E74
#007CB0
#008F4C
#F89F6D
#F499B8
Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 5 / 31

6. Automatic layout in variable data printing
HP chiclet HP chiclet
old palette new palette
automatic
layout
Buy HP Buy HP
computers workstation xw 8600
Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 6 / 31

7. The wide xvYCC gamut
Luma Gamut of xvYCC
Y
254
Over White
1.0 235
1< R’,G’,B’ 1< R’,G’,B’ BT.709-5
(sRGB)
sYCC
Extended Region
Extended Region
0 < R’,G’,B’ < 1 xvYCC
(Gamut of BT.709-5)
(sRGB)
R’,G’,B’< 0 R’,G’,B’< 0
0.0 16
-0.57 - 0.5 Black +0.5 +0.56
1
128
Cb, Cr
1 16 240 254
Extended Extended Chroma
Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 7 / 31

8. Applications of colour naming
Better user experience in GUIs
Automatic nudging of text and logo colours for readability in
variable data printing
Gamut mapping for HDR and wide gamut displays
Culture-independent preferred color rendering
Thematic rendering
Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 8 / 31

9. 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
Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 9 / 31

10. Local colour differences
y
520
530
0.8
540
510
550
Stiles Line Element
Ellipses plotted 3 x
560
0.6
570
500
580
590
0.4 600
610
620
490 630
700
0.2
480
470
0
460 x
45
0 0.2 0.4 0.6
Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 10 / 31

11. Categorisation
Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 11 / 31

12. Categorical perception
Definition (Stevan Harnad)
A categorical perception effect occurs when
1 a set of stimuli ranging along a physical continuum is given one
label on one side of a category boundary and another label on the
other side and
2 the subject can discriminate smaller physical differences between
pairs of stimuli that straddle boundary than between pairs that are
entirely within one category or the other
v’
v’
1. 1. 1.
white yellow
white yellow
.98 .92 .65 .53 orange
1. 1. 1.
.73 87 .91.87
.55 green1. .99 .98 .71.53 .75.88
orange
.97 .88 .62 .96.98 .98.96
.98 .92 .65 .53
.59.73 .74.57 .93
.97 .91 .94 .33 .45.51
.84 .66 .5 .46.44 A .58.54 .63.59
.73 87 .91
.31.49.63 .52 .5
green 1. .99 .98 .97 .88
.71 .71 .68 .78.81 .63.47
.7 .44 .47.52 .33.39.61 red
.55 .87 .71.53
.41.35.56
.57.56 .44 .61.61.52
B
.33 .45.56 .56 .53.53
.47.47
.75.88 .96
.43.32.46
aqua .77.63.38.39.56C E D
.64.53.35
.56.68 .76
.74.75.58 peach
.62 .59.73 .98 .98.96
.45 .53 .7 .8
.82.65.36.45 .85.87.82.74
.45.48 .3
.74.57.33
.52.74.81.87
.93
.69.42 .36 .91.89 .86
.47.48.28
.97 .91 .94 .45.51 .58
.33.57.69.83
A
.53.64 .5 .92.88.87
.38.37.48.44
gray .62.75
.84 .66 .5 .46 .54 .63.59.52
.57 .7 .82.86
.53.35.56.56
.47 .56.68
.88 pink
.44 .31.49
.72.74
.5
.74.82.53 .54.67
.77.59 .48
.35 .51.63
.63 .78 .81 .63
.9 .84 .48
.71 .71 .68
red
.7 .83 .8
.72.65 .5
.92.89.53
.7 .44 .47 .47 .33.39.61
.82.92 .9 .75.69
.52.41.35
.98 .9 .52
.83.93 .89.81
purple
blue .98 .9 .53 .83.94.92
.57.56 .44 .56.61.61
B .52 .53.53
.97 .9 .52.91
.95
.25
.33 .45.56 .56
.96.91 .6 .84 A = CIE Standard Illuminant A
.97.92.63
.43.32.46 .47.47
B = CIE Standard Illuminant B
aqua .77.63.38.39.56C E
C = CIE Standard Illuminant C
D .56 .68 .76
.97.94 D = CIE Standard Illuminant D65
E = equal-energy point
.74.75.58 peach
.97
u’
.15
.64.53.35
.53 .7 .8
.15 .35 .45
.45
.05 .25
.82 .65.36.45 .85 .87.82.74
David L. Post, 1988
.45.48 .3
.52.74.81
.87 .91.89
.69.42 .36 robust categorical colour perception
Beretta, Moroney, Recker (HP Labs) .47
Towards .48.28 .86 AIC 2009 12 / 31

13. Global colour differences — how to find them?
broken warm white A = 20
cement grey Roman ochre Pompeian yellow
100
90
80
70
60
50 Indian orange
V
40 orange ochre
30 Arsigont
20 brown beige
10
Anatolian brown
0
0 10 20 30 40 50 60 70 80 90 100
T
Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 13 / 31

14. 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
Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 14 / 31

15. Colour ontogeny of languages
Brent Berlin and Paul Kay, University of Berkeley, 1969
The physiology underlying even the unique hues is unknown
There is no natural categorisation
orange
and/or
green yellow
white pink
and red blue brown and/or
black purple
and/or
yellow green
gray
I II III IV V VI VII
Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 15 / 31

16. Development of colour naming
Colour naming is acquired, not genetic
socio-economic status (SES)
Franklin et al., PNAS 105(9): 3221–3225, 2008
900 550
infants within-category adults
initiation time [ms]
initiation time [ms]
between-category
800
within-category 450
between-category
700
350
600
500 250
left right left right
visual field visual field
Occurs late in child’s development, but age is decreasing with
increase of technology
1900: basic four colours @ 8 years
1950: @ 5 years of age
Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 16 / 31

17. 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
Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 17 / 31

18. Goal
1 Large dictionary
currently harvesting unconstrained names
extensive, through crowd-sourcing
evolves through time
not limited to one language
2 Number of synonym categories 12
decided though crowd-sourcing
not 266 like in ISCC–NBS thesaurus
. . . or 26, or 30, or 80. . .
3 Algorithm for determining categories
construct separate categorisations for each colour patch
explicitly ask user for a specific and a general name
explore boundary-finding algorithms
Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 18 / 31

19. Multilingual colour naming experiment
http://www.hpl.hp.com/personal/Nathan_Moroney/mlcn.html
Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 19 / 31

20. The colour thesaurus
http://www.hpl.hp.com/personal/Nathan_Moroney/color-thesaurus.html
Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 20 / 31

21. From colour naming to thesaurus
conventional exclusionary
usage corpus
naming typographic
raw corpus deletions substitutions spell-checker
experiment harmonization
inverse YCiCii for each substring
antonyms merging
neighbors name statistics
CIECAM02
synonyms
neighbors threshold
core frequency scrubbed
number unique
vocabulary analysis corpus
IP addresses
Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 21 / 31

22. Contributed name distribution
2000 green
blue
light blue
lavender
grass green
tan
khaki
dark brown
cornflower
cream
purple gray yellow green dark purple red orange
pink navy blue navy moss green dark red
red maroon peach burnt orange chocolate
1600 black
lime green
lime
dark blue
burgundy
salmon
spring green
pea green
crimson
coral
brown dark green light purple baby blue apple green
magenta lilac rose kelly green eggplant
violet olive gold dark pink goldenrod
sky blue olive green plum rust medium blue
1200 orange
yellow
cyan
periwinkle
brick red
beige
blue green
fluorescent green
ocean blue
leaf green
teal mint green mustard sage bright purple
light green bright green white hunter green grape
fuchsia mauve indigo pale green light yellow
800 turquoise
aqua
sea green
hot pink
bright blue
chartreuse
blue gray
cobalt
emerald
jade
royal blue neon green light brown midnight blue ochre
forest seafoam aquamarine light pink army green
brick
400
0 gre r m y t l m l p s g p r b b k b k fl b c c e l e b
en ed agen ellow urquo ight b aroo ilac eriwi ea gr rass g each ose eige right haki urnt elly g uores lue g ornflo hocol ggpla eaf gr mera rick
ta ise lue n nkl een re blu ora ree ce ray we ate nt ee ld
e en e nge n nt g r n
ree
n
Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 22 / 31

23. Handling missing names
naming corpus
raw corpus
experiment scrubbing
name lexical
harvesting analysis
FALSE
crowd name synonyms and
TRUE
found antonyms
Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 23 / 31

24. Expanding the corpus
Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 24 / 31

25. Robustness: qualifying the corpus
Problems:
we observed about
3% disruptive
participants in the
experiment
variability of rarely
used names
Solution is to collect
explicit feedback on the
global statistics from
each participant
More efficient than
recruiting domain
specialists
Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 25 / 31

26. Feedback distribution
correct, spot on
good
neutral
poor
wrong, completely wrong
Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 26 / 31

27. 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
Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 27 / 31

28. What we have so far
Framework for collecting colour names in multiple languages
Robust: colour thesaurus served 194’369 color names as of
Wednesday the 23rd of September 2009
Robust: feedback mechanism for improving the corpus quality
with use
Mechanism to harvest less common names
Still cheating on categorisation: synthetic synonyms vs.
categories, but making progress . . .
Experimenting with linguistic analysis tools to find category
boundaries
No user interface yet to collect category names and antonyms
Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 28 / 31

29. Is the category hierarchy just 2 deep?
broken warm white
cement grey
Anatolian brown
Roman ochre
yellowish orange 1 brown beige
yellowish orange 2 Arsigont
yellow orange 1 Pompeian yellow
orange orange 2
orange ochre
Indian ochre
red orange 3
violet reddish orange 1
reddish orange 2
blue
green1
green2
Coloroid color
369 names +
7 domains 48 basics
79 synonyms
Beretta, Moroney, Recker (HP Labs) Towards robust categorical colour perception AIC 2009 29 / 31

30. Height
0 1 2 3 4
shiny
plastic
gloss
sticky
medium.weight
medium
photo
waxy
ultra
flat
coated
vinyl
smooth
Beretta, Moroney, Recker (HP Labs)
dull
diffuse
off.white
semi
semi.gloss
transparent
clear
thin
light.weight
Albuquerque, November 2009
pale
ivory
yellow
cream
beige
soft
eggshell
chalky
blue
green
gray
satin
decorative
grain
texture
rough
brown
pearl
Towards robust categorical colour perception
metallic
silver
high
parchment
Dendrogram — good things to come . . .
tan
office
matte
paper
white
art
rigid
surface
color
canvas
linen
fine
pattern
heavy.weight
AIC 2009
heavy
card
thick
stiff
see: Moroney & Beretta,“Nominal scaling of print substrates,” CIC 17,
bright
gold
30 / 31

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