Envisioning information
A tool for dynamic explorations of color mappings
I3D '90 Proceedings of the 1990 symposium on Interactive 3D graphics
Choosing effective colours for data visualization
Proceedings of the 7th conference on Visualization '96
Digital Color Imaging Handbook
Digital Color Imaging Handbook
A rule-based tool for assisting colormap selection
VIS '95 Proceedings of the 6th conference on Visualization '95
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Interactive Color Palette Tools
IEEE Computer Graphics and Applications
Color Design for Illustrative Visualization
IEEE Transactions on Visualization and Computer Graphics
Learning color names for real-world applications
IEEE Transactions on Image Processing
Crowdsourcing graphical perception: using mechanical turk to assess visualization design
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Color compatibility from large datasets
ACM SIGGRAPH 2011 papers
ReVision: automated classification, analysis and redesign of chart images
Proceedings of the 24th annual ACM symposium on User interface software and technology
Do Mechanical Turks dream of square pie charts?
Proceedings of the 3rd BELIV'10 Workshop: BEyond time and errors: novel evaLuation methods for Information Visualization
Color naming models for color selection, image editing and palette design
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The efficacy of human post-editing for language translation
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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We 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.