Visual Object Categorization via Sparse Representation

Huanzhang Fu 1 Chao Zhu 1 Emmanuel Dellandréa 1 Charles-Edmond Bichot 1 Liming Chen 1
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : In this paper, we consider the problem of classifying a real world image to the corresponding object class based on its visual content via sparse representation, which is originally used as a powerful tool for acquiring, representing and compressing high-dimensional signals. Assuming the intuitive hypothesis that an image could be represented by a linear combination of the training images from the same class, we propose a novel approach for visual object categorization in which a sparse representation of the image is first of all obtained by solving a l1 or l0-minimization problem and then fed into a traditional classifier such as Support Vector Machine (SVM) to finally perform the specified task. Experimental results obtained on the SIMPLIcity database have shown that this new approach can improve the classification performance compared to standard SVM using directly features extracted from the image.
Type de document :
Communication dans un congrès
International Conference on Image and Graphics (ICIG), Sep 2009, Xi'an, China. IEEE, pp.943-948, 2009, 〈10.1109/ICIG.2009.100〉
Liste complète des métadonnées
https://hal.archives-ouvertes.fr/hal-01437773
Contributeur : Équipe Gestionnaire Des Publications Si Liris <>
Soumis le : mardi 17 janvier 2017 - 13:58:54
Dernière modification le : jeudi 19 avril 2018 - 14:38:06

Identifiants

Collections

EC-LYON | UNIV-LYON2 | LIRIS | INSA-LYON | UNIV-LYON1

Citation

Huanzhang Fu, Chao Zhu, Emmanuel Dellandréa, Charles-Edmond Bichot, Liming Chen. Visual Object Categorization via Sparse Representation. International Conference on Image and Graphics (ICIG), Sep 2009, Xi'an, China. IEEE, pp.943-948, 2009, 〈10.1109/ICIG.2009.100〉. 〈hal-01437773〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

124

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD