1. Color naming
A Computational model of Color Perception and Color Naming, Johann Lammens, Buffalo CS Ph.D. dissertation
Cross language study of Berlin and Kay, 1969
“Basic colors”

2. 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)
Ask audience to give basic colors they can identify in Berlin Kay colors (adapted by Lammel)

3. Color naming
“Basic colors”
Vary with language

4. 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

5. 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

6. 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.