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.
CITATION STYLE
Luo, B., Robles-Kelly, A., Torsello, A., Wilson, R. C., & Hancock, E. R. (2001). Discovering shape categories by clustering shock trees. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2124, pp. 152–160). Springer Verlag. https://doi.org/10.1007/3-540-44692-3_19
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