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Shape Recognition with Spectral Distances
May 2011 (vol. 33 no. 5)
pp. 1065-1071
Michael M. Bronstein, Technion - Israel Institute of Technology, Haifa
Alexander M. Bronstein, Tel-Aviv University, Tel-Aviv
Recent works have shown the use of diffusion geometry for various pattern recognition applications, including nonrigid shape analysis. In this paper, we introduce spectral shape distance as a general framework for distribution-based shape similarity and show that two recent methods for shape similarity due to Rustamov and Mahmoudi and Sapiro are particular cases thereof.

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Index Terms:
Diffusion distance, commute time, spectral distance, eigenmap, Laplace-Beltrami operator, heat kernel, distribution, global point signature, nonrigid shapes, similarity.
Citation:
Michael M. Bronstein, Alexander M. Bronstein, "Shape Recognition with Spectral Distances," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 5, pp. 1065-1071, May 2011, doi:10.1109/TPAMI.2010.210
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