Revisiting Perceptually Optimized Color Mapping for High-Dimensional Data Analysis

Mittelstädt, Sebastian; Bernard, Jürgen; Schreck, Tobias; Steiger, Martin; Kohlhammer, Jörn; Keim, Daniel A.

Revisiting Perceptually Optimized Color Mapping for High-Dimensional Data Analysis

Dateien

Mittelstaedt_1-61dd2213295e45201.pdfGröße: 1.79 MBDownloads: 172

Datum

2014

Autor:innen

Mittelstädt, Sebastian

Bernard, Jürgen

Schreck, Tobias

Steiger, Martin

Kohlhammer, Jörn

Keim, Daniel A.

Open Access-Veröffentlichung

Open Access Green

Sammlungen

Informatik und Informationswissenschaft

Publikationstyp

Beitrag zu einem Konferenzband

Publikationsstatus

Published

Erschienen in

MARGIT POHL ..., , ed.. EuroVis 2014 : the Eurographics Conference on Visualization ; 9-13 June 2014, Swansea, Wales, UK ; EuroVis Short Papers. Eurographics Association, 2014, pp. 91-95. ISBN 978-3-905674-69-9. Available under: doi: 10.2312/eurovisshort.20141163

Zusammenfassung

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.

Fachgebiet (DDC)

004 Informatik

Konferenz

EuroVis 2014 : the Eurographics Conference on Visualization, 9. Juni 2014 - 13. Juni 2014, Swansea, UK

Zitieren

ISO 690

MITTELSTÄDT, Sebastian, Jürgen BERNARD, Tobias SCHRECK, Martin STEIGER, Jörn KOHLHAMMER, Daniel A. KEIM, 2014. Revisiting Perceptually Optimized Color Mapping for High-Dimensional Data Analysis. EuroVis 2014 : the Eurographics Conference on Visualization. Swansea, UK, 9. Juni 2014 - 13. Juni 2014. In: MARGIT POHL ..., , ed.. EuroVis 2014 : the Eurographics Conference on Visualization ; 9-13 June 2014, Swansea, Wales, UK ; EuroVis Short Papers. Eurographics Association, 2014, pp. 91-95. ISBN 978-3-905674-69-9. Available under: doi: 10.2312/eurovisshort.20141163

BibTex

@inproceedings{Mittelstadt2014Revis-30003,
  year={2014},
  doi={10.2312/eurovisshort.20141163},
  title={Revisiting Perceptually Optimized Color Mapping for High-Dimensional Data Analysis},
  isbn={978-3-905674-69-9},
  publisher={Eurographics Association},
  booktitle={EuroVis 2014 : the Eurographics Conference on Visualization ; 9-13 June 2014, Swansea, Wales, UK ; EuroVis Short Papers},
  pages={91--95},
  editor={Margit Pohl ...},
  author={Mittelstädt, Sebastian and Bernard, Jürgen and Schreck, Tobias and Steiger, Martin and Kohlhammer, Jörn and Keim, Daniel A.}
}

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Universitätsbibliographie

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