Learning shape categories by clustering shock trees

This paper investigates whether meaningful shape categories can be identified in an unsupervised way by clustering shocktrees. We commence by computing weighted and unweighted edit distances between shock-trees extracted from the HamiltonJacobi skeleton of 2D binary shapes. Next we use an EMlike 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.