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Multimedia Tools and Applications

, Volume 77, Issue 20, pp 26173–26189 | Cite as

A robust CBIR framework in between bags of visual words and phrases models for specific image datasets

  • Achref Ouni
  • Thierry UrrutyEmail author
  • Muriel Visani
Article
  • 204 Downloads

Abstract

One objective of the Content Based Image Retrieval research field is to propose new methodologies and tools to manage the increasing number of images available. Linked to a specific context of small expert datasets without prior knowledge, our research work focuses on improving the discriminative power of the image representation while keeping the same efficiency for retrieval. Based on the well-known bag of visual words model, we propose three different methodologies inspired by the visual phrase model effectiveness and a compression technique which ensures the same effectiveness for retrieval than the BoVW model. Our experimental results study the performance of our proposals on different well known benchmark datasets and show its good performance compared to other recent approaches.

Keywords

Image representation CBIR Bag of visual words Visual phrases Expert dataset 

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 ax 1 of the NUMERIC Program.

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.XLIM, UMR CNRS 7252University of PoitiersPoitiersFrance
  2. 2.Laboratory L3iUniversity of La RochelleLa RochelleFrance
  3. 3.Vietnam-France ICT LaboratoryUniversity of Science and Technology of HanoïHanoiVietnam
  4. 4.Laboratory LaBRIUniversity of BordeauxBordeauxFrance

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