On the editing distance between unordered labeled trees
Information Processing Letters
Active shape models—their training and application
Computer Vision and Image Understanding
Matching Hierarchical Structures Using Association Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shock Graphs and Shape Matching
International Journal of Computer Vision
Mean and maximum common subgraph of two graphs
Pattern Recognition Letters
Modal Matching for Correspondence and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficiently Computing Weighted Tree Edit Distance Using Relaxation Labeling
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Wormholes in Shape Space: Tracking through Discontinuous Changes in Shape
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Decision trees for filtering large databases of graphs
International Journal of Intelligent Systems Technologies and Applications
Theoretical analysis and experimental comparison of graph matching algorithms for database filtering
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
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In this paper we investigate how to construct a shape space for sets of shock trees. To do this we construct a super-tree to span the union of the set of shock trees. We learn this super-tree and the correspondences of the node in the sample trees using a maximizing likelihood approach. We show that the likelihood is maximized by the set of correspondences that minimizes the sum of the tree edit distance between pair of trees, subject to edge consistency constraints. Eachno de of the super-tree corresponds to a dimension of the pattern space. Individual suchtrees are mapped to vectors in this pattern space.