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Non-rigid 3D shape classification using Bag-of-Feature techniques

Hedi Tabia 1 Olivier Colot 2 Mohamed Daoudi 3, 4 Jean-Philippe Vandeborre 3, 4, *
* Corresponding author
2 LAGIS-SI
LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
3 LIFL - FOX MIIRE
LIFL - Laboratoire d'Informatique Fondamentale de Lille
Abstract : 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.
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Hedi Tabia, Olivier Colot, Mohamed Daoudi, Jean-Philippe Vandeborre. Non-rigid 3D shape classification using Bag-of-Feature techniques. IEEE International Conference on Multimedia and Expo (ICME), Jul 2011, Barcelona, Spain. pp.475. ⟨hal-00666732⟩

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