Low-level numerical characteristics and inductive learning methodology in texture recognition

A method for applying inductive learning to the texture recognition problem is proposed. The method is based on a three-level generalization for the description of texture classes. The first step, scaling interface, is to transform local texture features into their higher symbolic representation as numerical intervals. The second step is the incorporation of the AQ inductive learning algorithm in order to find description rules. The third step is to apply the SG-TRUNC method for rule optimization. The medium recognition ratio for this method was over 90%, and all classes of texture were recognized. In comparison, the k-NN pattern recognition method failed to recognize all classes of textures and had a recognition ratio of 83%