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Adaptive relevant feature selection and hierarchical image classification in heterogeneous databases

Abstract : In heterogeneous databases, images often provided from different sources and belong to different topics, hence there is a need for a large description to ensure efficient representation of their content. However, extracted features are not always adapted to the considered image database. In this paper we propose a new image recognition approach based on two innovations, namely adaptive feature selection and Multi-Model Classification Method (MC-MM). The adaptive selection considers only the most adapted features with the used image database content. The MC-MM method ensures image recognition using hierarchically selected features. Experimental results confirm the effectiveness and the robustness of our proposed approach.
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https://hal.archives-ouvertes.fr/hal-00745315
Contributor : Frédéric Davesne <>
Submitted on : Thursday, October 25, 2012 - 11:48:38 AM
Last modification on : Tuesday, June 30, 2020 - 11:56:08 AM

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Rostom Kachouri, Khalifa Djemal, Hichem Maaref. Adaptive relevant feature selection and hierarchical image classification in heterogeneous databases. Traitement du Signal, Lavoisier, 2011, 28 (5), pp.547--574. ⟨10.3166/TS.28.547-574⟩. ⟨hal-00745315⟩

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