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.
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https://hal.univ-cotedazur.fr/hal-01322768
Contributor : Denis Pallez <>
Submitted on : Friday, May 27, 2016 - 4:17:19 PM
Last modification on : Monday, November 5, 2018 - 3:52:10 PM

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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 Region. pp.2849-2852. ⟨hal-01322768⟩

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