Selecting Semantically-Resonant Colors for Data Visualization

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dc.contributor.authorLin, Sharonen_US
dc.contributor.authorFortuna, Julieen_US
dc.contributor.authorKulkarni, Chinmayen_US
dc.contributor.authorStone, Maureenen_US
dc.contributor.authorHeer, Jeffreyen_US
dc.contributor.editorB. Preim, P. Rheingans, and H. Theiselen_US
dc.date.accessioned2015-02-28T15:31:42Z
dc.date.available2015-02-28T15:31:42Z
dc.date.issued2013en_US
dc.description.abstractWe introduce an algorithm for automatic selection of semantically-resonant colors to represent data (e.g., using blue for data about ''oceans'', or pink for ''love''). Given a set of categorical values and a target color palette, our algorithm matches each data value with a unique color. Values are mapped to colors by collecting representative images, analyzing image color distributions to determine value-color affinity scores, and choosing an optimal assignment. Our affinity score balances the probability of a color with how well it discriminates among data values. A controlled study shows that expert-chosen semantically-resonant colors improve speed on chart reading tasks compared to a standard palette, and that our algorithm selects colors that lead to similar gains. A second study verifies that our algorithm effectively selects colors across a variety of data categories.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12127en_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.subjectH.5.m [Information Interfaces]en_US
dc.subjectMiscen_US
dc.subjectColoren_US
dc.titleSelecting Semantically-Resonant Colors for Data Visualizationen_US
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32-Issue 3
EuroVis13: Eurographics Conference on Visualization