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Computational Model of Color Perception and Naming

Explore a computational model of color perception and naming inspired by Berlin and Kay's study of basic colors, adapting statistical pattern recognition to account for boundaries across languages.

mireille-clarke
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Computational Model of Color Perception and Naming

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  1. 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”

  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)

  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.

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