The multiple-point variogram of images for robust texture classification

Most texture analysis techniques require training data to perform classification or retrieval of images. In many practical situations, the amount of data representing different texture classes can be too limited to satisfy the training of a reliable classifier. Therefore, finding an effective feature of texture is very useful to cope with a variety of applications. This paper presents the extension of the two-point variogram to multiple-point variogram of images for texture feature extraction, which is also robust to noise and computationally economic. The matching of the variogram functions for pattern classification can be enhanced with the use of a spectral distortion measure without the requirement of training data. Experimental results and comparison with other methods, which require training data, suggest the usefulness of the proposed approach.