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Multimedia Tools and Applications

, Volume 77, Issue 5, pp 5475–5501 | Cite as

Vector space model adaptation and pseudo relevance feedback for content-based image retrieval

  • H. Karamti
  • M. Tmar
  • M. Visani
  • T. Urruty
  • F. Gargouri
Article

Abstract

Image retrieval is an important problem for researchers in computer vision and content-based image retrieval (CBIR) fields. Over the last decades, many image retrieval systems were based on image representation as a set of extracted low-level features such as color, texture and shape. Then, systems calculate similarity metrics between features in order to find similar images to a query image. The disadvantage of this approach is that images visually and semantically different may be similar in the low level feature space. So, it is necessary to develop tools to optimize retrieval of information. Integration of vector space models is one solution to improve the performance of image retrieval. In this paper, we present an efficient and effective retrieval framework which includes a vectorization technique combined with a pseudo relevance model. The idea is to transform any similarity matching model (between images) to a vector space model providing a score. A study on several methodologies to obtain the vectorization is presented. Some experiments have been undertaken on Wang, Oxford5k and Inria Holidays datasets to show the performance of our proposed framework.

Keywords

Vectorization CBIR Neural network Late fusion Vector space model Pseudo-relevance feedback 

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.National School of Engineers of SfaxUniversity of SfaxSfaxTunisia
  2. 2.Institut Supérieur d’Informatique et de Multimédia de SfaxSfaxTunisia
  3. 3.Computer Science Lab (L3i)University of La RochelleLa RochelleFrance
  4. 4.Vietnam-France, ICT Lab, USTHHanoiFrance
  5. 5.University of PoitiersPoitiersFrance

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