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Revisiting Perceptually Optimized Color Mapping for High-Dimensional Data Analysis

Mittelstädt, Sebastian and Bernard, Jürgen and Schreck, Tobias and Steiger, Martin and Kohlhammer, Jörn and Keim, Daniel A. (2014):
Revisiting Perceptually Optimized Color Mapping for High-Dimensional Data Analysis.
Eurographics Association, Goslar, In: EuroVis - Short Papers 2014, DOI: 10.2312/eurovisshort.20141163, [Conference or Workshop Item]

Abstract

Colors is one of the most effective visual variables since it can be combined with other mappings and encode information without using any additional space on the display. An important example where expressing additional visual dimensions is direly needed is the analysis of high-dimensional data. The property of perceptual linearity is desirable in this application, because the user intuitively perceives clusters and relations among multi-dimensional data points. Many approaches use two-dimensional colormaps in their analysis, which are typically created by interpolating in RGB, HSV or CIELAB color spaces. These approaches share the problem that the resulting colors are either saturated and discriminative but not perceptual linear or vice versa. A solution that combines both advantages has been previously introduced by Kaski et al.; yet, this method is to date underutilized in Information Visualization according to our literature analysis. The method maps high-dimensional data points into the CIELAB color 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 analysis of high-dimensional data. Further, we evaluate the method and provide guidelines for different analysis tasks.

Item Type: Conference or Workshop Item
Erschienen: 2014
Creators: Mittelstädt, Sebastian and Bernard, Jürgen and Schreck, Tobias and Steiger, Martin and Kohlhammer, Jörn and Keim, Daniel A.
Title: Revisiting Perceptually Optimized Color Mapping for High-Dimensional Data Analysis
Language: English
Abstract:

Colors is one of the most effective visual variables since it can be combined with other mappings and encode information without using any additional space on the display. An important example where expressing additional visual dimensions is direly needed is the analysis of high-dimensional data. The property of perceptual linearity is desirable in this application, because the user intuitively perceives clusters and relations among multi-dimensional data points. Many approaches use two-dimensional colormaps in their analysis, which are typically created by interpolating in RGB, HSV or CIELAB color spaces. These approaches share the problem that the resulting colors are either saturated and discriminative but not perceptual linear or vice versa. A solution that combines both advantages has been previously introduced by Kaski et al.; yet, this method is to date underutilized in Information Visualization according to our literature analysis. The method maps high-dimensional data points into the CIELAB color 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 analysis of high-dimensional data. Further, we evaluate the method and provide guidelines for different analysis tasks.

Publisher: Eurographics Association, Goslar
Uncontrolled Keywords: Business Field: Visual decision support, Research Area: Human computer interaction (HCI), Color models, Color perception, Coloring, Color analysis, Color difference evaluations, Information visualization, Visual analytics
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: EuroVis - Short Papers 2014
Date Deposited: 12 Nov 2018 11:16
DOI: 10.2312/eurovisshort.20141163
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