Structural Pattern Recognition: A Random Graph Approach
This paper presents the notion of random graphs and their associated probability distributions. It addresses both the structural and probabilistic aspects of structural pattern recognition. A structural pattern can be explicitly represented in the form of attributed graphs and an ensemble of such representations can be considered as outcomes of a mapping, called random graph mapping. To account for the variation of structural patterns in the ensemble, a lower order probability distribution is used to approximate the high order joint probability. To synthesize an ensemble of attributed graphs into a probability distribution (or a set of distributions) of a random graph, we introduce a distance measure together with a hierarchical clustering algorithm. The distance measure is defined as the minimum change of a specially defined Shannon’s entropy before and after the merging of the distributions. With this new formulation, both supervised and unsupervised classification of structural patterns can be achieved.
KeywordsRandom Graph Structural Pattern Random Element Random String Neighbor Rule
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