Co-occurrence based features for automatic texture classification using neural networks

摘要

Abstract: In this paper some of the commonly used features for texture classification based on co-occurrence statistics are studied. First, the classification capabilities of individual features in classifying among a small and a large number of texture images are evaluated. Then, the capabilities of different combinations of texture features are examined in order to establish a reduced set of features for maximum performance. An artificial neural network is used to test the suitability of promising feature groups for texture classification. It is shown that the features considered may be broadly divided into two groups in terms of their classification performance. It is also shown that with a judicious choice of features and a well trained neural network classifier, high recognition rates can be achieved. !13