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Discovering Shape Categories by Clustering Shock Trees

  • B. Luo
  • A. Robles-Kelly
  • A. Torsello
  • R. C. Wilson
  • E. R. Hancock
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2124)

Abstract

This paper investigates whether meaningful shape categories can be identified in an unsupervised way by clustering shock-trees. We commence by computing weighted and unweighted edit distances between shock-trees extracted from the Hamilton-Jacobi skeleton of 2D binary shapes. Next we use an EM-like algorithm to locate pairwise clusters in the pattern of edit-distances. We show that when the tree edit distance is weighted using the geometry of the skeleton, then the clustering method returns meaningful shape categories.

Keywords

clustering shock trees EM algorithm Hamilton-Jacobi skeleton 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • B. Luo
    • 1
  • A. Robles-Kelly
    • 1
  • A. Torsello
    • 1
  • R. C. Wilson
    • 1
  • E. R. Hancock
    • 1
  1. 1.Department of Computer ScienceUniversity of YorkYorkUK

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