Abstract
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.
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References
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© 1987 Springer-Verlag Berlin Heidelberg
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Wong, A.K.C. (1987). Structural Pattern Recognition: A Random Graph Approach. In: Devijver, P.A., Kittler, J. (eds) Pattern Recognition Theory and Applications. NATO ASI Series, vol 30. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-83069-3_26
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DOI: https://doi.org/10.1007/978-3-642-83069-3_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-83071-6
Online ISBN: 978-3-642-83069-3
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