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Volume: 28 | Article ID: art00036
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Choice of distance metrics for RGB color image analysis
  DOI :  10.2352/ISSN.2470-1173.2016.20.COLOR-349  Published OnlineFebruary 2016
Abstract

Many image clustering algorithms use distance metric in the process of taking decision. When dealing with color images, a distance metric will be used to decide whether two pixels or regions are closed. Colorimetric distances proposed by CIE(Commission Internationale de l'Eclairage) are often used in Lab color space because it is a uniform chromaticity space. However, RGB color space is useful to image processing and instead of converting color image from RGB to another color space before processing, it might be interesting to have the same or better results without changing the color space. In our work, we implement different distance metrics and compare the result of k-mean clustering algorithm in RGB color space to the one in L*a*b* with the colorimetric distance. Two evaluation criteria have been used and we conclude that being in RGB color space and choosing adequately the distance metric, we obtain better segmentation results.

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Amadou T. Sanda Mahama, Augustin S. Dossa, Pierre Gouton, "Choice of distance metrics for RGB color image analysisin Proc. IS&T Int’l. Symp. on Electronic Imaging: Color Imaging XXI: Displaying, Processing, Hardcopy, and Applications,  2016,  https://doi.org/10.2352/ISSN.2470-1173.2016.20.COLOR-349

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Copyright © Society for Imaging Science and Technology 2016
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Electronic Imaging
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