Boosting Kernel Combination for multi-class image categorization

Alexis Lechervy 1 Philippe-Henri Gosselin 2 Frédéric Precioso 3
2 MIDI
ETIS - Equipes Traitement de l'Information et Systèmes
3 Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe KEIA
SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : In this paper, we propose a novel algorithm to design multi- class kernel functions based on an iterative combination of weak kernels in a scheme inspired from boosting framework. The method proposed in this article aims at building a new feature where the centroid for each class are optimally lo- cated. We evaluate our method for image categorization by considering a state-of-the-art image database and by compar- ing our results with reference methods. We show that on the Oxford Flower databases our approach achieves better results than previous state-of-the-art methods.
Type de document :
Communication dans un congrès
2012 IEEE International Conference on Image Processing (ICIP), Sep 2012, Orlando, United States. IEEE, pp.4, 2012


https://hal.archives-ouvertes.fr/hal-00753156
Contributeur : Philippe-Henri Gosselin <>
Soumis le : samedi 17 novembre 2012 - 18:50:32
Dernière modification le : mardi 28 octobre 2014 - 18:41:56
Document(s) archivé(s) le : lundi 18 février 2013 - 03:42:38

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Alexis Lechervy, Philippe-Henri Gosselin, Frédéric Precioso. Boosting Kernel Combination for multi-class image categorization. 2012 IEEE International Conference on Image Processing (ICIP), Sep 2012, Orlando, United States. IEEE, pp.4, 2012. <hal-00753156>

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