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Combining Multimodal Preferences for Multimedia Information Retrieval

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Published in Proceedings of the 9th ACM SIGMM International Workshop on Multimedia Information Retrieval, MIR 2007. Augsburg (Germany) - Sept. 24-29 - . 2007, p. 71-78
Abstract Representing and fusing multimedia information is a key issue to discover semantics in multimedia. In this paper we address more specifically the problem of multimedia content retrieval by first defining a novel preference-based representation particularly adapted to the fusion problem, and then, by investigating the RankBoost algorithm to combine those preferences and a learn multimodal retrieval model. The approach has been tested on annotated images and on the complete TRECVID 2005 corpus and compared with SVMbased fusion strategies. The results show that our approach equals SVM performance but, contrary to SVM, is parameter free and faster.
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Research groups Computer Vision and Multimedia Laboratory
Viper group
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BRUNO, Eric, KLUDAS, Jana, MARCHAND-MAILLET, Stéphane. Combining Multimodal Preferences for Multimedia Information Retrieval. In: Proceedings of the 9th ACM SIGMM International Workshop on Multimedia Information Retrieval, MIR 2007. Augsburg (Germany). [s.l.] : [s.n.], 2007. p. 71-78. https://archive-ouverte.unige.ch/unige:47725

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Deposited on : 2015-03-06

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