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.
Domaines
Machine Learning [stat.ML]| Origine | Fichiers éditeurs autorisés sur une archive ouverte |
|---|
Loading...