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

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.
Type de document :
Communication dans un congrès
Content-Based Multimedia Indexing (CBMI), 2013 11th International Workshop on, 2013, Veszprém, Hungary. pp.41-46, 2013
Liste complète des métadonnées

https://hal.inria.fr/hal-00953084
Contributeur : Marie-Christine Fauvet <>
Soumis le : vendredi 28 février 2014 - 10:31:41
Dernière modification le : jeudi 11 janvier 2018 - 06:21:05

Identifiants

  • HAL Id : hal-00953084, version 1

Collections

Citation

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〉

Partager

Métriques

Consultations de la notice

129