Three-dimensional object retrieval based on vector quantization of invariant descriptors

Hedi Tabia; Olivier Colot; Mohamed Daoudi; Jean-Philippe Vandeborre

doi:10.1117/1.JEI.21.2.023011

21 May 2012 Three-dimensional object retrieval based on vector quantization of invariant descriptors

Hedi Tabia, Olivier Colot, Mohamed Daoudi, Jean-Philippe Vandeborre

Author Affiliations +

Journal of Electronic Imaging, Vol. 21, Issue 2, 023011 (May 2012). https://doi.org/10.1117/1.JEI.21.2.023011

Abstract

A novel method for three-dimensional (3-D) shape retrieval using bag-of-feature techniques (BoF) is proposed. This method is based on vector quantization of invariant descriptors of 3-D object patches. Firstly, it starts by selecting and then describing a set of points from the 3-D 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 weighted vector counting the occurrences of every word is computed. These results clearly demonstrate that the method is robust to nonrigid 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, and Jean-Philippe Vandeborre "Three-dimensional object retrieval based on vector quantization of invariant descriptors," Journal of Electronic Imaging 21(2), 023011 (21 May 2012). https://doi.org/10.1117/1.JEI.21.2.023011

Published: 21 May 2012

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