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Color Naming Models for Color Selection, Image Editing and Palette Design

Jeffrey Heer, Maureen Stone. Proc. ACM Human Factors in Computing Systems (CHI), 2012
Figure for Color Naming Models for Color Selection, Image Editing and Palette Design
Color Saliency Map. Each point represents a 5×5×5 bin in CIE L*a*b* color space; slices for every other L* value are shown. The area of each point is proportional to its saliency: large points indicate consistently named colors, small points indicate colors that exhibit high naming confusion. Interestingly, visible clusters correspond to basic color terms identified by Berlin & Kay (blue, brown, green, grey, pink, purple, etc).
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Abstract
Our ability to reliably name colors provides a link between visual perception and symbolic cognition. In this paper, we investigate how a statistical model of color naming can enable user interfaces to meaningfully mimic this link and support novel interactions. We present a method for constructing a probabilistic model of color naming from a large, unconstrained set of human color name judgments. We describe how the model can be used to map between colors and names and define metrics for color saliency (how reliably a color is named) and color name distance (the similarity between colors based on naming patterns). We then present a series of applications that demonstrate how color naming models can enhance graphical interfaces: a color dictionary & thesaurus, name-based pixel selection methods for image editing, and evaluation aids for color palette design.
BibTeX
@inproceedings{2012-color-naming-models,
  title = {Color Naming Models for Color Selection, Image Editing and Palette Design},
  author = {Heer, Jeffrey AND Stone, Maureen},
  booktitle = {Proc. ACM Human Factors in Computing Systems (CHI)},
  year = {2012},
  url = {https://idl.uw.edu/papers/color-naming-models},
  doi = {10.1145/2207676.2208547}
}