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Communication Dans Un Congrès Année : 2015

A factorized model for multiple SVM and multi-label classification for large scale multimedia indexing

Résumé

This paper presents a set of improvements for SVM-based large scale multimedia indexing. The proposed method is particularly suited for the detection of many target concepts at once and for highly imbalanced classes (very infrequent concepts). The method is based on the use of multiple SVMs (MSVM) for dealing with the class imbalance and on some adaptations of this approach in order to allow for an efficient implementation using optimized linear algebra routines. The implementation also involves hashed structures allowing the factorization of computations between the multiple SVMs and the multiple target concepts, and is denoted as Factorized-MSVM. Experiments were conducted on a large-scale dataset, namely TRECVid 2012 semantic indexing task. Results show that the Factorized-MSVM performs as well as the original MSVM, but it is significantly much faster. Speed-ups by factors of several hundreds were obtained for the simultaneous classification of 346 concepts, when compared to the original MSVM implementation using the popular libSVM implementation.
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Dates et versions

hal-01230720 , version 1 (18-11-2015)

Identifiants

Citer

Bahjat Safadi, Georges Quénot. A factorized model for multiple SVM and multi-label classification for large scale multimedia indexing. 13th International Workshop on Content-Based Multimedia Indexing (CBMI), Jun 2015, Prague, Czech Republic. pp.1-6, ⟨10.1109/CBMI.2015.7153610⟩. ⟨hal-01230720⟩
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