Hybdrid Content Based Image Retrieval combining multi-objective interactive genetic algorithm and SVM

Abstract : The amount of images contained in repositories or available on Internet has exploded over the last years. In order to retrieve efficiently one or several images in a database, the development of Content-Based Image Retrieval (CBIR) systems has become an intensively active research area. However, most proposed systems are keyword-based and few imply the end-user during the search (through relevance feedback). Visual low-level descriptors are then substituted to keywords but there is a gap between visual description and user expectations. We propose a new framework which combines a multi-objective interactive genetic algorithm, allowing a trade-off between image features and user evaluations, and a support vector machine to learn the user relevance feedback. We test our system on SIMPLIcity database, commonly used in the literature to evaluate CBIR systems using a genetic algorithm, and it outperforms the recent frameworks.
Type de document :
Communication dans un congrès
Pattern Recognition (ICPR), 2012 21st International Conference on, 2012, Unknown, Unknown or Invalid Region. pp.2849-2852, 2012
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https://hal-unice.archives-ouvertes.fr/hal-01322768
Contributeur : Denis Pallez <>
Soumis le : vendredi 27 mai 2016 - 16:17:19
Dernière modification le : samedi 28 mai 2016 - 01:13:45

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  • HAL Id : hal-01322768, version 1

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R. Pighetti, Denis Pallez, Frédéric Precioso. Hybdrid Content Based Image Retrieval combining multi-objective interactive genetic algorithm and SVM. Pattern Recognition (ICPR), 2012 21st International Conference on, 2012, Unknown, Unknown or Invalid Region. pp.2849-2852, 2012. <hal-01322768>

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