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Texture Retrieval Using Scattering Coefficients and Probability Product Kernels

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Latent Variable Analysis and Signal Separation (LVA/ICA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9237))

Abstract

In this paper we introduce a content based image retrieval system that leverages the benefits of the scattering transform as a means of feature extraction. To measure similarity between feature vectors, we adapt a probability product kernel and derive an approximate version which can be implemented efficiently. The proposed approach achieves a retrieval performance superior to comparable filterbank transform systems.

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Notes

  1. 1.

    http://www.gol.ei.tum.de/fileadmin/w00bhl/www/texture_retrieval_scattering_15.zip.

  2. 2.

    http://www-cvr.ai.uiuc.edu/ponce_grp/data/.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Technische Universität München, Munich, Germany

    Alexander Sagel, Dominik Meyer & Hao Shen

Authors
  1. Alexander Sagel
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  2. Dominik Meyer
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  3. Hao Shen
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Corresponding author

Correspondence to Alexander Sagel .

Editor information

Editors and Affiliations

  1. Inria, Villers-les-Nancy, France

    Emmanuel Vincent

  2. Tel Aviv University, Tel-Aviv, Israel

    Arie Yeredor

  3. Technical University of Libere, Liberec, Czech Republic

    Zbyněk Koldovský

  4. The Czech Academy of Sciences, Prague, Czech Republic

    Petr Tichavský

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Sagel, A., Meyer, D., Shen, H. (2015). Texture Retrieval Using Scattering Coefficients and Probability Product Kernels. In: Vincent, E., Yeredor, A., Koldovský, Z., Tichavský, P. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2015. Lecture Notes in Computer Science(), vol 9237. Springer, Cham. https://doi.org/10.1007/978-3-319-22482-4_59

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  • DOI: https://doi.org/10.1007/978-3-319-22482-4_59

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22481-7

  • Online ISBN: 978-3-319-22482-4

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