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Adaptive and Optimal Combination of Local Features for Image Retrieval

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Book cover MultiMedia Modeling (MMM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10133))

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Abstract

With the large number of local feature detectors and descriptors in the literature of Content-Based Image Retrieval (CBIR), in this work we propose a solution to predict the optimal combination of features, for improving image retrieval performances, based on the spatial complementarity of interest point detectors. We review several complementarity criteria of detectors and employ them in a regression based prediction model, designed to select the suitable detectors combination for a dataset. The proposal can improve retrieval performance even more by selecting optimal combination for each image (and not only globally for the dataset), as well as being profitable in the optimal fitting of some parameters. The proposal is appraised on three state-of-the-art datasets to validate its effectiveness and stability. The experimental results highlight the importance of spatial complementarity of the features to improve retrieval, and prove the advantage of using this model to optimally adapt detectors combination and some parameters.

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Notes

  1. 1.

    http://www.robots.ox.ac.uk/~vgg/data/parisbuildings/.

  2. 2.

    http://www.robots.ox.ac.uk/~vgg/data/oxbuildings/.

  3. 3.

    https://lear.inrialpes.fr/~jegou/data.php.

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Acknowledgments

The authors are grateful to Nicéphore Cité, Institut national de l’information géographique et forestière (IGN) and French project POEME ANR-12-CORD-0031 for the financial support.

Author information

Authors and Affiliations

  1. University Paris-Est, LASTIG MATIS, IGN, ENSG, 94160, Saint-Mande, France

    Neelanjan Bhowmik, Valérie Gouet-Brunet & Lijun Wei

  2. Nicéphore Cité, 34 Quai Saint-Cosme, 71100, Chalon-sur-Saône, France

    Neelanjan Bhowmik & Gabriel Bloch

Authors
  1. Neelanjan Bhowmik
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  2. Valérie Gouet-Brunet
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  3. Lijun Wei
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  4. Gabriel Bloch
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Corresponding author

Correspondence to Neelanjan Bhowmik .

Editor information

Editors and Affiliations

  1. CNRS–IRISA, Rennes, France

    Laurent Amsaleg

  2. Reykjavík University, Reykjavik, Iceland

    Gylfi Þór Guðmundsson

  3. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  4. Reykjavik University, Reykjavik, Ireland

    Björn Þór Jónsson

  5. National Institute of Informatics, Tokyo, Japan

    Shin’ichi Satoh

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Bhowmik, N., Gouet-Brunet, V., Wei, L., Bloch, G. (2017). Adaptive and Optimal Combination of Local Features for Image Retrieval. In: Amsaleg, L., Guðmundsson, G., Gurrin, C., Jónsson, B., Satoh, S. (eds) MultiMedia Modeling. MMM 2017. Lecture Notes in Computer Science(), vol 10133. Springer, Cham. https://doi.org/10.1007/978-3-319-51814-5_7

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  • DOI: https://doi.org/10.1007/978-3-319-51814-5_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-51813-8

  • Online ISBN: 978-3-319-51814-5

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