Title
Categorical Colormap Optimization with Visualization Case Studies.
Abstract
Mapping a set of categorical values to different colors is an elementary technique in data visualization. Users of visualization software routinely rely on the default colormaps provided by a system, or colormaps suggested by software such as ColorBrewer. In practice, users often have to select a set of colors in a semantically meaningful way (e.g., based on conventions, color metaphors, and logological associations), and consequently would like to ensure their perceptual differentiation is optimized. In this paper, we present an algorithmic approach for maximizing the perceptual distances among a set of given colors. We address two technical problems in optimization, i.e., (i) the phenomena of local maxima that halt the optimization too soon, and (ii) the arbitrary reassignment of colors that leads to the loss of the original semantic association. We paid particular attention to different types of constraints that users may wish to impose during the optimization process. To demonstrate the effectiveness of this work, we tested this technique in two case studies. To reach out to a wider range of users, we also developed a web application called Colourmap Hospital.
Year
DOI
Venue
2017
10.1109/TVCG.2016.2599214
IEEE Trans. Vis. Comput. Graph.
Keywords
Field
DocType
Image color analysis,Color,Optimization,Data visualization,Visualization,Extraterrestrial measurements
Computer vision,Data visualization,Information visualization,Computer science,Categorical variable,Visualization,Maxima and minima,Software,Artificial intelligence,Web application,Software visualization
Journal
Volume
Issue
ISSN
23
1
1077-2626
Citations 
PageRank 
References 
6
0.45
18
Authors
6
Name
Order
Citations
PageRank
Hui Fang111414.47
Simon J. Walton2385.44
E. Delahaye360.45
Harris, C.J.492.38
D. A. Storchak560.45
Min Chen6129382.69