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Color theme or color palette can deeply influence the quality and the feelingof a photograph or a graphical design. Although color palettes may come fromdifferent sources such as online crowd-sourcing, photographs and graphicaldesigns, in this paper, we consider color palettes extracted from fine artcollections, which we believe to be an abundant source of stylistic and uniquecolor themes. We aim to capture color styles embedded in these collections bymeans of statistical models and to build practical applications upon thesemodels. As artists often use their personal color themes in their paintings,making these palettes appear frequently in the dataset, we employed densityestimation to capture the characteristics of palette data. Via densityestimation, we carried out various predictions and interpolations on palettes,which led to promising applications such as photo-style exploration, real-timecolor suggestion, and enriched photo recolorization. It was, however,challenging to apply density estimation to palette data as palettes often comeas unordered sets of colors, which make it difficult to use conventionalmetrics on them. To this end, we developed a divide-and-conquer sortingalgorithm to rearrange the colors in the palettes in a coherent order, whichallows meaningful interpolation between color palettes. To confirm theperformance of our model, we also conducted quantitative experiments ondatasets of digitized paintings collected from the Internet and receivedfavorable results.

Color Orchestra: Ordering Color Palettes for Interpolation and Prediction