An Optimal Probabilistic Graphical Model for Point Set Matching

  • Tibério S. Caetano
  • Terry Caelli
  • Dante A. C. Barone
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3138)

Abstract

We present a probabilistic graphical model for point set matching. By using a result about the redundancy of the pairwise distances in a point set, we represent the binary relations over a simple triangulated graph that retains the same informational content as the complete graph. The maximal clique size of this resultant graph is independent of the point set sizes, what enables us to perform exact inference in polynomial time with a Junction Tree algorithm. The resulting technique is optimal in the Maximum a Posteriori sense. Experiments show that the algorithm significantly outperforms standard probabilistic relaxation labeling.

Keywords

Markov Random Field Maximal Clique Domain Pattern Probabilistic Graphical Model Junction Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Tibério S. Caetano
    • 1
    • 2
  • Terry Caelli
    • 1
  • Dante A. C. Barone
    • 2
  1. 1.Department of Computing ScienceUniversity of AlbertaEdmontonCanada
  2. 2.Instituto de InformáticaUnivesidade Federal do Rio Grande do SulPorto AlegreBrazil

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