Histogram of Radon transform and texton matrix for texture analysis and classification

Abstract

In this study, the authors introduce a new and efficient method to classify texture images. From the histogram of the Radon transform, a texture orientation matrix is obtained and combined with a texton matrix for generating a new type of co-occurrence matrix. From the co-occurrences matrix, 20 statistical features for texture images classification have been extracted: seven statistics of the first-level order and 13 of the second-level one. K-Nearest neighbour and support vector machine models are used for classification. The proposed approach has been tested on widely used texture datasets (Brodatz and University KTH Royal Institute of Technology Textures under varying Illumination, Pose and Scale) and compared with several different alternative methods. The experimental results show a very high-accuracy level, confirming the strength of the developed method which overcomes the state-of-the-art methods for texture classification.