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Categorical colormap optimization with visualization case studies

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journal contribution
posted on 14.01.2019, 15:02 by Hui FangHui Fang, S. Walton, E. Delahaye, J. Harris, D.A. Storchak, Min Chen
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.

History

School

  • Science

Department

  • Computer Science

Published in

IEEE Transactions on Visualization and Computer Graphics

Volume

23

Issue

1

Pages

871 - 880

Citation

FANG, H. ... et al., 2016. Categorical colormap optimization with visualization case studies. IEEE Transactions on Visualization and Computer Graphics, 23(1), pp. 871 - 880.

Publisher

© Institute of Electrical and Electronics Engineers (IEEE)

Version

AM (Accepted Manuscript)

Acceptance date

01/08/2016

Publication date

2016-08-10

Notes

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

ISSN

1077-2626

eISSN

1941-0506

Language

en