Shape recognition with spectral distances

Michael M. Bronstein, Alexander M. Bronstein

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number5661779
Pages (from-to)1065-1071
Number of pages7
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume33
Issue number5
DOIs
StatePublished - 2011
Externally publishedYes

Keywords

  • Diffusion distance
  • Laplace-Beltrami operator
  • commute time
  • distribution
  • eigenmap
  • global point signature
  • heat kernel
  • nonrigid shapes
  • similarity
  • spectral distance

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