“Color compatibility from large datasets” by O’Donovan, Hertzmann and Agarwala

  • ©Peter O’Donovan, Aaron Hertzmann, and Aseem Agarwala




    Color compatibility from large datasets



    This paper studies color compatibility theories using large datasets, and develops new tools for choosing colors. There are three parts to this work. First, using on-line datasets, we test new and existing theories of human color preferences. For example, we test whether certain hues or hue templates may be preferred by viewers. Second, we learn quantitative models that score the quality of a five-color set of colors, called a color theme. Such models can be used to rate the quality of a new color theme. Third, we demonstrate simple proto-types that apply a learned model to tasks in color design, including improving existing themes and extracting themes from images.


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