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3D Object Classification Using Scale Invariant Heat Kernels with Collaborative Classification
Mostafa Abdelrahman1, Moumen El-Melegy1, 2, and Aly Farag1
1Computer Vision and Image Processing Laboratory, University of Louisville, Louisville, KY 40292, USA
mostafa.abdelrahman@louisville.edu
moumen.elmelegy@louisville.edu
aly.farag@louisville.edu
http://www.cvip.uofl.edu/
2Electrical Engineering Department, Assiut University, Assiut 71516, Egypt
Abstract. One of the major goals of computer vision is the development of flexible and efficient methods for shape representation. This paper proposes an approach for shape matching and retrieval based on scale-invariant heat kernel (HK). The approach uses a novel descriptor based on the histograms of the scale-invariant HK for a number of critical points on the shape at different time scales. We propose an improved method to introduce scale-invariance of HK to avoid noise-sensitive operations in the original method. A collaborative classification (CC) scheme is then employed for object classification. For comparison we compare our approach to well-known approaches on a standard benchmark dataset: the SHREC 2011. The results have indeed confirmed the high performance of the proposed approach on the shape retrieval problem.
Keywords: Heat kernels, shape retrieval, collaborative classification, 3D shape descriptors
LNCS 7583, p. 22 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012