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    Revisiting Perceptually Optimized Color Mapping for High-Dimensional Data Analysis

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    Date
    2014
    Author
    Mittelstädt, Sebastian
    Bernard, Jürgen ORCID
    Schreck, Tobias ORCID
    Steiger, Martin
    Kohlhammer, Jörn
    Keim, Daniel A. ORCID
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    Abstract
    Color is one of the most effective visual variables since it can be combined with other mappings and encodeinformation without using any additional space on the display. An important example where expressing additionalvisual dimensions is direly needed is the analysis of high-dimensional data. The property of perceptual linearity isdesirable in this application, because the user intuitively perceives clusters and relations among multi-dimensionaldata points. Many approaches use two-dimensional colormaps in their analysis, which are typically created byinterpolating in RGB, HSV or CIELAB color spaces. These approaches share the problem that the resulting colorsare either saturated and discriminative but not perceptual linear or vice versa. A solution that combines bothadvantages has been previously introduced by Kaski et al.; yet, this method is to date underutilized in InformationVisualization according to our literature analysis. The method maps high-dimensional data points into the CIELABcolor space by maintaining the relative perceived distances of data points and color discrimination. In this paper,we generalize and extend the method of Kaski et al. to provide perceptual uniform color mapping for visual analysisof high-dimensional data. Further, we evaluate the method and provide guidelines for different analysis tasks.
    BibTeX
    @inproceedings {10.2312:eurovisshort.20141163,
    booktitle = {EuroVis - Short Papers},
    editor = {N. Elmqvist and M. Hlawitschka and J. Kennedy},
    title = {{Revisiting Perceptually Optimized Color Mapping for High-Dimensional Data Analysis}},
    author = {Mittelstädt, Sebastian and Bernard, Jürgen and Schreck, Tobias and Steiger, Martin and Kohlhammer, Jörn and Keim, Daniel A.},
    year = {2014},
    publisher = {The Eurographics Association},
    ISBN = {978-3-905674-69-9},
    DOI = {10.2312/eurovisshort.20141163}
    }
    URI
    http://dx.doi.org/10.2312/eurovisshort.20141163
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    Eurographics Association copyright © 2013 - 2022 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
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