Discovering Shape Categories by Clustering Shock Trees

Authors:
Bin Luo;Antonio Robles-Kelly;Andrea Torsello;Richard C. Wilson;Edwin R. Hancock
Affiliations:
-;-;-;-;-
Venue:
CAIP '01 Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns
Year:
2001

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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.