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Découverte et exploitation d'objets visuels fréquents dans des collections multimédia

Abstract : The main goal of this thesis is to discover frequent visual objects in large multimedia collections. As in many areas (finance, genetics, . . .), it consists in extracting a knowledge, using the occurence frequency of an object in a collection as a relevance criterion. A first contribution is to provide a formalism to the problems of mining and discovery of frequent visual objects. The second contribution is a generic method to solve these two problems, based on an iterative sampling process, and on an efficient and scalable rigid objects matching. The third contribution of this work focuses on building a likelihood function close to the perfect distribution. Experiments show that contrary to state-of-the-art methods, our approach allows to discover efficiently very small objects in several millions images. Finally, several applications are presented, including trademark logos discovery, transmedia events detection or visual-based query suggestion.
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Submitted on : Monday, August 10, 2015 - 5:02:07 PM
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  • HAL Id : tel-01183699, version 1



Pierre Letessier. Découverte et exploitation d'objets visuels fréquents dans des collections multimédia. Base de données [cs.DB]. Télécom ParisTech, 2013. Français. ⟨NNT : 2013ENST0014⟩. ⟨tel-01183699⟩



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