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Identifying Relevant Features of Images from Their 2-D Topology

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Advances in Knowledge Discovery and Management

Part of the book series: Studies in Computational Intelligence ((SCI,volume 398))

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Abstract

Inspired by the abilities of the human brain to identify elements from eyesight, the problem of dimensionality reduction in the domain of visual perception consists to extract a few number of features from an image database in order to recognize them. This paper presents an innovative feature extractor for images that considers their two dimensional topology to identify their relevant features. Numerical experiment applied to 70000 pictures representing handwritten digits, 698 images illustrating the face of a person under different poses and lighting directions, or 355 color holidays photos demonstrate the accuracy of our approach to drastically reduce the dimension while conserving the intelligible relations between data objects, reaching a classification of better quality from the reduced version of images than from their original full-size realizations.

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Authors and Affiliations

  1. CIRRELT - Université de Montréal, C.P. 6128, succ. Centre-Ville, Montréal, Québec, Canada, H3C 3J7

    Marc Joliveau

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  1. Marc Joliveau
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Correspondence to Marc Joliveau .

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Editors and Affiliations

  1. , LINA (CNRS UMR 6241), Nantes University, Cedex 3, rue C. Pauc BP 50609, Nantes, 44306, France

    Fabrice Guillet

  2. , Department of Econometrics, Université de Genève, room 5232, Uni-Mail, 40, bd du Pont-d'Arve, Geneva, 1211, Switzerland

    Gilbert Ritschard

  3. Laboratoire ERIC, Université Lumière Lyon 2, Bât L., avenue Pierre Mendès-France 5, Bron, 69600, France

    Djamel Abdelkader Zighed

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Joliveau, M. (2012). Identifying Relevant Features of Images from Their 2-D Topology. In: Guillet, F., Ritschard, G., Zighed, D. (eds) Advances in Knowledge Discovery and Management. Studies in Computational Intelligence, vol 398. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25838-1_10

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  • DOI: https://doi.org/10.1007/978-3-642-25838-1_10

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25837-4

  • Online ISBN: 978-3-642-25838-1

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