Color naming

• A Computational model of Color Perception and Color Naming, Johann Lammens, Buffalo CS Ph.D. dissertation http://www.cs.buffalo.edu/pub/colornaming/diss/diss.html

• Cross language study of Berlin and Kay, 1969

• “Basic colors”

Color naming

• “Basic colors”– Meaning not predicted from parts (e.g. blue,

yellow, but not bluish)– not subsumed in another color category, (e.g. red

but not crimson or scarlet)– can apply to any object (e.g. brown but not

blond)– highly meaningful across informants (red but not

chartruese)

Color naming

Num clrs colors2 White, black3 White, black, red4 White, black, red, green | yellow5 White, black, red, green, yellow6 White, black, red, green, yellow, blue7 White, black, red, green, yellow, blue, brown

8-11 White, black, red, green, yellow, blue, brown, purple | pink | orange | gray

• “Basic colors”– Vary with language

Color naming

• Berlin and Kay experiment:– Elicit all basic color terms from 329 Munsell

chips (40 equally spaced hues x 8 values plus 9 neutral hues

– Find best representative– Find boundaries of that

term

Color naming

• Berlin and Kay experiment:– Representative (“focus” constant across lang’s)

• Boundaries vary even across subjects and trials

• Lammens fits a linear+sigmoid model to each of R-B B-Y and Brightness data from macaque monkey LGN data of DeValois et. al.(1966) to get a color model. As usual this is two chromatic and one achromatic

Color naming

• To account for boundaries Lammens used standard statistical pattern recognition with the feature set determined by the coordinates in his color space defined by macaque LGN opponent responses.

• Has some theoretical but no(?) experimental justification for the model.