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Download fileCategorical colormap optimization with visualization case studies
journal contribution
posted on 2019-01-14, 15:02 authored by Hui FangHui Fang, S. Walton, E. Delahaye, J. Harris, D.A. Storchak, Min ChenMapping 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 GraphicsVolume
23Issue
1Pages
871 - 880Citation
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
2016-08-01Publication date
2016-08-10Notes
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
1077-2626eISSN
1941-0506Publisher version
Language
- en