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Model-based similarity estimation of multidimensional temporal sequences

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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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Notes

  1. Note that this method might overestimate dissimilarity if the natural path contains a significant amount of nondiagonal parts.

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Authors and Affiliations

  1. IRISA / ENS Cachan, Campus de Beaulieu, 35 042, Rennes Cedex, France

    Romain Tavenard

  2. CNRS / IRISA, Campus de Beaulieu, 35 042, Rennes Cedex, France

    Laurent Amsaleg & Guillaume Gravier

Authors
  1. Romain Tavenard
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  2. Laurent Amsaleg
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  3. Guillaume Gravier
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Correspondence to Romain Tavenard.

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Tavenard, R., Amsaleg, L. & Gravier, G. Model-based similarity estimation of multidimensional temporal sequences. Ann. Telecommun. 64, 381–390 (2009). https://doi.org/10.1007/s12243-009-0091-4

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  • DOI: https://doi.org/10.1007/s12243-009-0091-4

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