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Communication Dans Un Congrès Année : 2004

Discriminative Classification vs Modeling Methods in CBIR

Résumé

Statistical learning methods are currently considered with an increasing interest in the content-based image retrieval (CBIR) community. We compare in this article two leader techniques for classification tasks. The first method uses one-class and two-class SVM to discriminate data. The second approach is based on Gaussian Mixture to model classes. To deal with the specificity of the CBIR classifica- tion task, adaptations have been proposed. Experimental tests on a generalist database have been carried out. Ad- vantages and drawbacks are discussed for each method.
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Dates et versions

hal-00520316 , version 1 (22-09-2010)

Identifiants

  • HAL Id : hal-00520316 , version 1
  • PRODINRA : 247270

Citer

Philippe-Henri Gosselin, Micheline Najjar, Matthieu Cord, Christophe Ambroise. Discriminative Classification vs Modeling Methods in CBIR. IEEE International Conference on Advanced Concepts for Intelligent Vision Systems, Sep 2004, Belgium. pp.1. ⟨hal-00520316⟩
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