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

[+] Author Affiliations
Hedi Tabia

University Lille 1, LAGIS UMR CNRS 8219, Villeneuve d’Ascq, France 59658

Mohamed Daoudi

Institut Telecom, LIFL UMR CNRS 8022, Telecom Lille 1, Villeneuve d’Ascq, France 59658

Olivier Colot

University Lille 1, LAGIS UMR CNRS 8219, Villeneuve d’Ascq, France 59658

Jean-Philippe Vandeborre

Institut Telecom, LIFL UMR CNRS 8022, Telecom Lille 1, Villeneuve d’Ascq, France 59658

J. Electron. Imaging. 21(2), 023011 (May 21, 2012). doi:10.1117/1.JEI.21.2.023011
History: Received March 19, 2011; Revised March 23, 2012; Accepted March 28, 2012
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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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© 2012 SPIE and IS&T

Citation

Hedi Tabia ; Mohamed Daoudi ; Olivier Colot and Jean-Philippe Vandeborre
"Three-dimensional object retrieval based on vector quantization of invariant descriptors", J. Electron. Imaging. 21(2), 023011 (May 21, 2012). ; http://dx.doi.org/10.1117/1.JEI.21.2.023011


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