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.
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© 2001 Springer-Verlag Berlin Heidelberg
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Luo, B., Robles-Kelly, A., Torsello, A., Wilson, R.C., Hancock, E.R. (2001). Discovering Shape Categories by Clustering Shock Trees. In: Skarbek, W. (eds) Computer Analysis of Images and Patterns. CAIP 2001. Lecture Notes in Computer Science, vol 2124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44692-3_19
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DOI: https://doi.org/10.1007/3-540-44692-3_19
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