Non-rigid 3D shape classification using ...
Document type :
Communication dans un congrès avec actes
Title :
Non-rigid 3D shape classification using Bag-of-Feature techniques
Author(s) :
Tabia, Hedi [Auteur]
Laboratoire d'Automatique, Génie Informatique et Signal [LAGIS]
Colot, Olivier [Auteur]
LAGIS-SI
Daoudi, Mohamed [Auteur]
FOX MIIRE [LIFL]
Vandeborre, Jean-Philippe [Auteur correspondant]
FOX MIIRE [LIFL]
Laboratoire d'Automatique, Génie Informatique et Signal [LAGIS]
Colot, Olivier [Auteur]

LAGIS-SI
Daoudi, Mohamed [Auteur]
FOX MIIRE [LIFL]
Vandeborre, Jean-Philippe [Auteur correspondant]

FOX MIIRE [LIFL]
Scientific editor(s) :
IEEE
Conference title :
IEEE International Conference on Multimedia and Expo (ICME)
City :
Barcelona
Country :
Espagne
Start date of the conference :
2011-07-11
Book title :
Proceedings of ICME 2011
Publication date :
2011-07
English keyword(s) :
Bag-of-Feature
3D-Shape
classification
3D-Shape
classification
HAL domain(s) :
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
English abstract : [en]
In this paper, we present a new method for 3D-shape categorization using Bag-of-Feature techniques (BoF). This method is based on vector quantization of invariant descriptors of 3D-object patches. We analyze the performance ...
Show more >In this paper, we present a new method for 3D-shape categorization using Bag-of-Feature techniques (BoF). This method is based on vector quantization of invariant descriptors of 3D-object patches. We analyze the performance of two well-known classifiers: the Naïve Bayes and the SVM. The results show the effectiveness of our approach and prove that the method is robust to non-rigid and deformable shapes, in which the class of transformations may be very wide due to the capability of such shapes to bend and assume different forms.Show less >
Show more >In this paper, we present a new method for 3D-shape categorization using Bag-of-Feature techniques (BoF). This method is based on vector quantization of invariant descriptors of 3D-object patches. We analyze the performance of two well-known classifiers: the Naïve Bayes and the SVM. The results show the effectiveness of our approach and prove that the method is robust to non-rigid and deformable shapes, in which the class of transformations may be very wide due to the capability of such shapes to bend and assume different forms.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
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