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Learning templates from fuzzy examples in structural pattern recognition

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1 Author(s)
Kwok-Ping Chan ; Dept. of Comput. Sci., Univ. of Hong Kong, Hong Kong

Fuzzy-Attribute Graph (FAG) was proposed to handle fuzziness in the pattern primitives in structural pattern recognition. FAG has the advantage that we can combine several possible definitions into a single template, and hence only one matching is required instead of one for each definition. Also, each vertex or edge of the graph can contain fuzzy attributes to model real-life situations. However, in our previous approach, we need a human expert to define the templates for the fuzzy graph matching. This is usually tedious, time-consuming and error-prone. In this paper, we propose a learning algorithm that will, from a number of fuzzy examples, each of them being a FAG, find the smallest template that can be matched to the given patterns with respect to the matching metric

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Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on  (Volume:26 ,  Issue: 1 )