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Categorical Colormap Optimization with Visualization Case Studies

Fang, H, Walton, S, Delahaye, E, Harris, J, Storchak, DA and Chen, M (2016) Categorical Colormap Optimization with Visualization Case Studies. IEEE Transactions on Visualization and Computer Graphics, 23 (1). pp. 871-880. ISSN 1077-2626

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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.

Item Type: Article
Uncontrolled Keywords: 0801 Artificial Intelligence And Image Processing, 0802 Computation Theory And Mathematics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science
Publisher: IEEE
Date Deposited: 25 Jun 2018 11:18
Last Modified: 25 Jun 2018 11:18
DOI or Identification number: 10.1109/TVCG.2016.2599214
URI: http://researchonline.ljmu.ac.uk/id/eprint/8890

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