Application of symbolic machine learning to the recognition of texture concepts

Abstract

This paper presents an approach to the texture recognition problem that deals with noisy learning and testing data. The method incorporates symbolic machine learning to acquire texture descriptions. Then, these descriptions are optimized in order to remove some nOisylimperfect components. We present methodology and experimental results showing the increase in system recognition effectiveness when optimization of texture descriptions is proceeded continuously. Such a matching of partial concept prototypes with test data gives a recognition characteristics obtained for different concept optimization degrees. Then. the dynamics of this characteristics is used to make recognition decision.