An Image Understanding System Using Attributed Symbolic Representation and Inexact Graph-Matching
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Algorithms for clustering data
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Active shape models—their training and application
Computer Vision and Image Understanding
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FORMS: a flexible object recognition and modeling system
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Shock Graphs and Shape Matching
International Journal of Computer Vision
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Central Clustering of Attributed Graphs
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A skeletal measure of 2D shape similarity
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Polynomial-Time Metrics for Attributed Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph clustering using the weighted minimum common supergraph
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Computer Vision and Image Understanding
Skeleton Search: Category-Specific Object Recognition and Segmentation Using a Skeletal Shape Model
International Journal of Computer Vision
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SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
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Maximum likelihood method for parameter estimation of bell-shaped functions on graphs
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Neurocomputing
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This paper poses the problem of tree-clustering as that of fitting a mixture of tree unions to a set of sample trees. The tree-unions are structures from which the individual data samples belonging to a cluster can be obtained by edit operations. The distribution of observed tree nodes in each cluster sample is assumed to be governed by a Bernoulli distribution. The clustering method is designed to operate when the correspondences between nodes are unknown and must be inferred as part of the learning process. We adopt a minimum description length approach to the problem of fitting the mixture model to data. We make maximum-likelihood estimates of the Bernoulli parameters. The tree-unions and the mixing proportions are sought so as to minimize the description length criterion. This is the sum of the negative logarithm of the Bernoulli distribution, and a message-length criterion that encodes both the complexity of the union-trees and the number of mixture components. We locate node correspondences by minimizing the edit distance with the current tree unions, and show that the edit distance is linked to the description length criterion. The method can be applied to both unweighted and weighted trees. We illustrate the utility of the resulting algorithm on the problem of classifying 2D shapes using a shock graph representation.