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Discovering shape categories by clustering shock trees
conference contribution
posted on 2001-01-01, 00:00 authored by B Luo, Antonio Robles-KellyAntonio Robles-Kelly, A Torsello, R C Wilson, E R HancockThis 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.
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Volume
2124Pagination
152 - 160Publisher DOI
ISSN
0302-9743eISSN
1611-3349ISBN-13
9783540425137Publication classification
E1.1 Full written paper - refereedTitle of proceedings
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Usage metrics
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