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Communication Dans Un Congrès Année : 2011

Adaptive N-normalization for enhancing music similarity

Résumé

The N-Normalization is an efficient method for normalizing a given similarity computed among multimedia objects. It can be considered for clustering and kernel enhancement. However, most approaches to N-Normalization parametrize the method arbitrarily in an ad-hoc manner. In this paper, we show that the optimal parameterization is tightly related to the geometry of the problem at hand. For that purpose , we propose a method for estimating an optimal parameteriza-tion given only the associated pair-wise similarities computed from any specific dataset. This allows us to normalize the similarity in a meaningful manner. More specifically, the proposed method allows us to improve retrieval performance as well as minimize unwanted phenomena such as hubs and orphans.
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Dates et versions

hal-01132539 , version 1 (17-03-2015)

Identifiants

Citer

Mathieu Lagrange, George Tzanetakis. Adaptive N-normalization for enhancing music similarity. IEEE ICASSP, May 2011, Prague, Czech Republic. ⟨10.1109/ICASSP.2011.5946422⟩. ⟨hal-01132539⟩
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