Classifier Fusion for SVM-Based Multimedia Semantic Indexing

Stéphane Ayache 1 Georges Quénot 1 Jérôme Gensel 2
1 MRIM - Modélisation et Recherche d’Information Multimédia [Grenoble]
LIG - Laboratoire d'Informatique de Grenoble, Inria - Institut National de Recherche en Informatique et en Automatique
2 LIG Laboratoire d'Informatique de Grenoble - STEAMER
LIG - Laboratoire d'Informatique de Grenoble
Abstract : Concept indexing in multimedia libraries is very useful for users searching and browsing but it is a very challenging research problem as well. Combining several modalities, features or concepts is one of the key issues for bridging the gap between signal and semantics. In this pa- per, we present three fusion schemes inspired from the classical early and late fusion schemes. First, we present a kernel-based fusion scheme which takes advantage of the kernel basis of classifiers such as SVMs. Second, we integrate a new normalization process into the early fusion scheme. Third, we present a contextual late fusion scheme to merge classification scores of several concepts. We conducted experiments in the framework of the official TRECVID'06 evaluation campaign and we obtained signif- icant improvements with the proposed fusion schemes relatively to usual fusion schemes.
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Stéphane Ayache, Georges Quénot, Jérôme Gensel. Classifier Fusion for SVM-Based Multimedia Semantic Indexing. ECIR 2007, 2007, Rome, Italy. ⟨hal-00953886⟩

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