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Selecting a Discriminant Subset of Co-occurrence Matrix Features for Texture-Based Image Retrieval

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Advances in Visual Computing (ISVC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3804))

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Abstract

In the general case, searching for images in a content-based image retrieval (CBIR) system amounts essentially, and unfortunately, to a sequential scan of the whole database. In order to accelerate this process, we want to generate summaries of the image database. In this paper, we focus on the selection of the texture features that will be used as a signature in our forthcoming system. We analysed the descriptors extracted from grey-level co-occurrence matrices’s (COM) under the constraints imposed by database systems.

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Author information

Authors and Affiliations

  1. Atlas-GRIM, INRIA & LINA (FRE CNRS 2729) Polytechnic School of the University of Nantes, BP 50609, 44306 cedex 03, Nantes, France

    Najlae Idrissi & José Martinez

  2. GSCM Science Faculty of Rabat, University Mohamed V Rabat, Morocco

    Najlae Idrissi & Driss Aboutajdine

Authors
  1. Najlae Idrissi
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  2. José Martinez
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  3. Driss Aboutajdine
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  4. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

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© 2005 Springer-Verlag Berlin Heidelberg

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Idrissi, N., Martinez, J., Aboutajdine, D. (2005). Selecting a Discriminant Subset of Co-occurrence Matrix Features for Texture-Based Image Retrieval. In: Bebis, G., Boyle, R., Koracin, D., Parvin, B. (eds) Advances in Visual Computing. ISVC 2005. Lecture Notes in Computer Science, vol 3804. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11595755_88

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  • DOI: https://doi.org/10.1007/11595755_88

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30750-1

  • Online ISBN: 978-3-540-32284-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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