Model-based similarity estimation of multidimensional temporal sequences

Romain Tavenard 1 Laurent Amsaleg 1, * Guillaume Gravier 2
* Corresponding author
1 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
2 METISS - Speech and sound data modeling and processing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : Content-based queries in multimedia sequence databases where information is sequential is a tough issue, especially when dealing with large-scale applications. One of the key points is similarity estimation between a query sequence and elements of the database. In this paper, we investigate two ways to compare multimedia sequences, one-that comes from the literature-being computed in the feature space while the other one is computed in a model space, leading to a representation less sensitive to noise. We compare these approaches by testing them on a real audio dataset, which points out the utility of working in the model space.
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Journal articles
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Submitted on : Tuesday, February 22, 2011 - 10:09:47 AM
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Romain Tavenard, Laurent Amsaleg, Guillaume Gravier. Model-based similarity estimation of multidimensional temporal sequences. Annals of Telecommunications - annales des télécommunications, Springer, 2009, 64 (5), pp.381-390. ⟨http://www.springerlink.com/content/95w75xu573ul1838/fulltext.pdf⟩. ⟨10.1007/s12243-009-0091-4⟩. ⟨inria-00567877⟩

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