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Supervised texture classification: color space or texture feature selection?

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Abstract

The color of pixels can be represented in different color spaces which take into account different properties. However, no color space is well-suited to the discrimination of all texture databases and the prior determination of such a space is not easy. In this paper, we compare the performances reached by two texture classification schemes that use color spaces: (a) the single color space selection approach, that defines a set of texture features and then selects the color space with which the texture features allow to reach the highest classification accuracy, (b) the multi-color space feature selection (MCSFS) approach, that selects texture features which have been processed from images coded into different color spaces. Experiments carried out with benchmark texture databases show that taking advantage simultaneously of the properties of several color spaces thanks to the MCSFS approach improves the rates of well-classified images with lower learning and decision processing times.

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Notes

  1. A wrapper approach is based on the rate of well-classified images.

  2. A filter approach only takes into account the training images and does not thus depend on the classifier decision rule.

  3. The notation \((C_1,C_2,C_3)\) is a generic one for the three-dimensional color spaces.

  4. Commission Internationale de l’Eclairage (International Commission on Illumination)

  5. The Outex set is available at the Outex web site as test suite Outex-TC-00013 (http://www.outex.oulu.fi/temp/).

  6. The VisTex set is available at the Outex web site as test suite Contrib-TC-00006 (http://www.outex.oulu.fi/temp/).

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Acknowledgments

This research is funded by “Pôle de Compétitivité Maud” and “Région Nord-Pas de Calais”.

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Authors and Affiliations

  1. Laboratoire LISIC-EA 4491, Université du Littoral Côte d’Opale, Maison de la Recherche Blaise Pascal, 50, rue Ferdinand Buisson, BP 719, 62228, Calais Cedex, France

    A. Porebski & N. Vandenbroucke

  2. Laboratoire LAGIS-UMR CNRS 8219, Université Lille 1-Sciences et Technologies-Cité Scientifique, Bâtiment P2, 59655, Villeneuve d’Ascq, France

    L. Macaire

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  1. A. Porebski
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  2. N. Vandenbroucke
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  3. L. Macaire
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Correspondence to A. Porebski.

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Porebski, A., Vandenbroucke, N. & Macaire, L. Supervised texture classification: color space or texture feature selection?. Pattern Anal Applic 16, 1–18 (2013). https://doi.org/10.1007/s10044-012-0291-9

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