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Improving the Discriminative Power of Bag of Visual Words Model

  • Achref Ouni
  • Thierry UrrutyEmail author
  • Muriel Visani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10133)

Abstract

With the exponential increase of image database, Content Based Image Retrieval research field has started a race to always propose more effective and efficient tools to manage massive amount of data. In this paper, we focus on improving the discriminative power of the well-known bag of visual words model. To do so, we present n-BoVW, an approach that combines visual phrase model effectiveness keeping the efficiency of visual words model with a binary based compression algorithm. Experimental results on widely used datasets (UKB, INRIA Holidays, Corel1000 and PASCAL 2012) show the effectiveness of the proposed approach.

Keywords

Bag of visual words Visual phrases Image retrieval 

Notes

Acknowledgments

This research is supported by the Poitou-Charentes Regional Founds for Research activities and the European Regional Development Founds (ERDF) inside the e-Patrimoine project from the axe 1 of the NUMERIC Program.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.XLIM, UMR CNRS 7252, University of PoitiersPoitiersFrance
  2. 2.Laboratory L3i, University of La RochelleLa RochelleFrance

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