Discovering shape classes using tree edit-distance and pairwise clustering
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...[Show more]
|Collections||ANU Research Publications|
|Source:||International Journal of Computer Vision|
|01_Torsello_Discovering_shape_classes_2007.pdf||828.77 kB||Adobe PDF||Request a copy|
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