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Proceedings Paper

Deformable shape retrieval using bag-of-feature techniques
Author(s): Hedi Tabia; Mohamed Daoudi; Jean-Philippe Vandeborre; Olivier Colot
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Paper 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.

Paper Details

Date Published: 27 January 2011
PDF: 7 pages
Proc. SPIE 7864, Three-Dimensional Imaging, Interaction, and Measurement, 78640P (27 January 2011); doi: 10.1117/12.872182
Show Author Affiliations
Hedi Tabia, LAGIS FRE, CNRS, Univ. de Lille 1 (France)
Mohamed Daoudi, LIFL, CNRS, Institut Telecom, Telecom Lille 1 (France)
Jean-Philippe Vandeborre, LIFL, CNRS, Institut Telecom, Telecom Lille 1 (France)
Olivier Colot, LAGIS FRE, CNRS, Univ. de Lille 1 (France)


Published in SPIE Proceedings Vol. 7864:
Three-Dimensional Imaging, Interaction, and Measurement
J. Angelo Beraldin; Ian E. McDowall; Atilla M. Baskurt; Margaret Dolinsky; Geraldine S. Cheok; Michael B. McCarthy; Ulrich Neuschaefer-Rube, Editor(s)

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