PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Discovering Shape Classes using Tree Edit-Distance and Pairwise Clustering
Andrea Torsello, Antonio Robles-Kelly and Edwin Hancock
International Journal of Comouter Vision Volume 72, Number 3, pp. 259-285, 2006. ISSN 0920-5691


This paper describes work aimed at the unsupervised learning of shape-classes from shock trees. We commence by considering how to compute the edit distance between weighted trees. We show how to transform the tree edit distance problem into a series of maximum weight clique problems, and show how to use relaxation labeling to find an approximate solution. This allows us to compute a set of pairwise distances between graph-structures. We show how the edit distances can be used to compute a matrix of pairwise affinities using χ2 statistics. We present a maximum likelihood method for clustering the graphs by iteratively updating the elements of the affinity matrix. This involves interleaved steps for updating the affinity matrix using an eigendecomposition method and updating the cluster membership indicators. We illustrate the new tree clustering framework on shock-graphs extracted from the silhouettes of 2D shapes.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Machine Vision
Learning/Statistics & Optimisation
ID Code:3531
Deposited By:Edwin Hancock
Deposited On:25 February 2008