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Communication Dans Un Congrès Année : 2012

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

Résumé

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.
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Dates et versions

hal-01322768 , version 1 (27-05-2016)

Identifiants

  • HAL Id : hal-01322768 , version 1

Citer

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 Region. pp.2849-2852. ⟨hal-01322768⟩
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