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dc.contributor.authorMittelstädt, Sebastianen_US
dc.contributor.authorBernard, Jürgenen_US
dc.contributor.authorSchreck, Tobiasen_US
dc.contributor.authorSteiger, Martinen_US
dc.contributor.authorKohlhammer, Jörnen_US
dc.contributor.authorKeim, Daniel A.en_US
dc.contributor.editorN. Elmqvist and M. Hlawitschka and J. Kennedyen_US
dc.date.accessioned2014-12-16T07:21:07Z
dc.date.available2014-12-16T07:21:07Z
dc.date.issued2014en_US
dc.identifier.isbn978-3-905674-69-9en_US
dc.identifier.urihttp://dx.doi.org/10.2312/eurovisshort.20141163en_US
dc.description.abstractColor 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.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectI.3.6 [Computer Graphics]en_US
dc.subjectMethodology andTechniquesen_US
dc.subjectStandardsen_US
dc.subjectI.3.3 [Computer Graphics]en_US
dc.subjectPicture/Image Generationen_US
dc.subjectDisplay Algorithmsen_US
dc.titleRevisiting Perceptually Optimized Color Mapping for High-Dimensional Data Analysisen_US
dc.description.seriesinformationEuroVis - Short Papersen_US


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