出版社：University of Malaya * Faculty of Computer Science and Information Technology
摘要：A new multiclass pattern classifier called ‘Test Feature Classifier’ is presented. It is based on training a recogniser by training samples of binary patterns and voting primitive scores depending on many trained templates called ‘test feature’, which serves as local evaluation of the features. The method is nonmetric and does not misclassify any patterns once learned previously. The twoclass version of test feature classifier was of high performance for searching textual region in complex images. In this paper, we extend it to handle multiclass problems and apply it for solving illclass problems in texture classification. We show the performance of the classifier on more than 1000 real images and compare it with a linear distancebased classifier and a nonlinear distancebased classifier. The experimental results of both simulations and real applications show that the proposed classifier has better performance than conventional ones.
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