Clustering based re-scoring for semantic indexing of multimedia documents.

Abdelkader Hamadi 1 Georges Quénot 1 Philippe Mulhem 1
1 LIG - Laboratoire d'Informatique de Grenoble
Abstract : This paper describes a new approach for multime- dia documents indexing and addresses the problem of automati- cally detecting a large number of visual concepts. Though using a multi-label approaches are used in some works, concepts detectors are often trained independently. We propose a model that takes into account the detection of not only a target concept but also other ones and regroups in terms of semantics similar samples. The expected benefit from such a combination is to consider the relationships between concepts in order to reclassify the results of an initial indexing system. Experiments on the TRECVID 2012 data are presented and discussed. Our method has significantly improved a quite good baseline system performance up to +6% on mean average precision.
Keywords : image classification image sensors indexing information retrieval learning (artificial intelligence) ontologies (artificial intelligence) semantic networks support vector machines video retrieval KNN classifier MAP measurement MSVM TRECVID 2012 data conce
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Communication dans un congrès
Content-Based Multimedia Indexing (CBMI), 2013 11th International Workshop on, 2013, Veszprém, Hungary. pp.41-46, 2013
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https://hal.inria.fr/hal-00953084
Contributeur : Marie-Christine Fauvet <>
Soumis le : vendredi 28 février 2014 - 10:31:41
Dernière modification le : jeudi 11 octobre 2018 - 08:48:01

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Abdelkader Hamadi, Georges Quénot, Philippe Mulhem. Clustering based re-scoring for semantic indexing of multimedia documents.. Content-Based Multimedia Indexing (CBMI), 2013 11th International Workshop on, 2013, Veszprém, Hungary. pp.41-46, 2013. 〈hal-00953084〉

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