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Adaptive and Optimal Combination of Local Features for Image Retrieval

  • Neelanjan BhowmikEmail author
  • Valérie Gouet-Brunet
  • Lijun Wei
  • Gabriel Bloch
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10133)

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.

Keywords

CBIR Interest points Feature combination Spatial complementarity Regression model 

Notes

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.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Neelanjan Bhowmik
    • 1
    • 2
    Email author
  • Valérie Gouet-Brunet
    • 1
  • Lijun Wei
    • 1
  • Gabriel Bloch
    • 2
  1. 1.University Paris-Est, LASTIG MATIS, IGN, ENSGSaint-MandeFrance
  2. 2.Nicéphore CitéChalon-sur-SaôneFrance

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