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Sungkil Lee, Mike Sips, and Hans-Peter Seidel

IEEE Trans. Vis. and Computer Graphics, 19(10), 1746–1757, 2013.
Invited to and presented at IEEE InfoVIS 2013, Atlanta, GA
Visualization techniques often use color to present categorical differences to a user. When selecting a color palette, the perceptual qualities of color need careful consideration. Large coherent groups visually suppress smaller groups, and are often visually dominant in images. This article introduces the concept of class visibility used to quantitatively measure the utility of a color palette to present coherent categorical structure to the user. We present a color optimization algorithm based on our class visibility metric to make categorical differences clearly visible to the user. We performed two user experiments on user preference and visual search to validate our visibility measure over a range of color palettes. The results indicate that visibility is a robust measure, and our color optimization can increase the effectiveness of categorical data visualizations.
Paper preprints, slides, supplementary materials, and Google Scholar entry
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@ARTICLE{lee13:vsalc, title={{Perceptually Driven Visibility Optimization for Categorical Data Visualization}}, author={Sungkil Lee and Mike Sips and Hans-Peter Seidel}, journal={{IEEE Trans. Vis. and Computer Graphics}}, volume={19}, number={10}, pages={1746--1757}, year={2013} }

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