<title>Feature selection in a machine learning system for texture classification</title>

Abstract

The goals of this research as presented in this paper are (1) to explore the application of a machine learning method to problems of computer vision through the development of an integrated system of computer vision and machine learning, and (2) to select good features in the texture classification domain by evaluating texture features with classification results obtained by the integrated system. The main key of the feature evaluation is to consider the effects of an increasing number of features in the integrated system. The research is concerned with the development of texture feature extraction methods in texture classification and the integration of machine learning and computer vision systems. The feature extraction methods include Markov random fields, co-occurrence matrices, and moments, convolutions, and neighboring. Total of 25 texture features is extracted by these methods for the experimentation. The machine learning methods used in this research is an AQ15 method for inductive concept learning from examples. The integrated system consists of image preprocessing, feature extraction, classification (AQ15), and feature selection modules. The paper presents (1) what are good selected features by evaluating the performance of the classification system when the number of features are increasing, (2) whether coefficients (features) of the Markov Random Fields are suitable for the texture classification by comparing them with the already selected features.