Abstract
Abstract We propose a design scheme for a hierarchical fuzzy pattern matching classifier (HFPMC) and apply it to the tire tread pattern recognition problem. In this design scheme, a binary decision tree is constructed at first by using fuzzy C-means (FCM) algorithm. At each node, a representative subset of features which can split best the labelled data into two dissimilar groups is selected from all the available features on the base of cluster validity. The cluster validity is evaluated under the two criteria. The one is the polarization degree, and the other is whether all the samples of a class belong to the same cluster or not. Then, a hierarchical cluster structure for the HFPMC is reconstructed by combining the successive nodes formed by the same representative subset of features. As the hierarchical classifier, is used a fuzzy pattern matching classifier in which the designer's intuitive knowledges about the pattern recognition problem can be easily incorporated. At each subhierarchy, the reference fuzzy sets and prototypes for the HFPMC are defined based on the cluster centers of the corresponding subhierarchy. The proposed design scheme is applied to the design of a HFPMC for the tire tread pattern recognition. The design procedure including feature extraction is described in detail. Experimental results show the usefulness of the proposed design scheme.
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