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Deformable Shape Retrieval using Bag-of-Feature techniques

Hedi Tabia 1 Mohamed Daoudi 2, 3 Jean-Philippe Vandeborre 2, 3, * Olivier Colot 4
* Corresponding author
2 LIFL - FOX MIIRE
LIFL - Laboratoire d'Informatique Fondamentale de Lille
4 LAGIS-SI
LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
Abstract : We present a novel method for 3D-shape matching using Bag-of-Feature techniques (BoF). The method starts by selecting and then describing a set of points from the 3D-object. Such descriptors have the advantage of being invariant to different transformations that a shape can undergo. Based on vector quantization, we cluster those descriptors to form a shape vocabulary. Then, each point selected in the object is associated to a cluster (word) in that vocabulary. Finally, a BoF histogram counting the occurrences of every word is computed. These results clearly demonstrate 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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Submitted on : Monday, February 6, 2012 - 11:08:25 AM
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Hedi Tabia, Mohamed Daoudi, Jean-Philippe Vandeborre, Olivier Colot. Deformable Shape Retrieval using Bag-of-Feature techniques. 3D Image Processing (3DIP) and Applications II, Jan 2011, San Francisco, United States. pp.7864B-28. ⟨hal-00666739⟩

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