New rotaion invariant features for texture classification

Classification of texture images invariant to similarity transformations (shift, rotation and scaling) is regarded as one of difficult tasks in image processing. In this paper, we present a theoretically and computationally efficient approach for rotation invariant texture classification. The feature extraction for a given image involves applying the log-polar transform to eliminate the rotation effects, followed by the ridgelet transform. The method is tested with 4670 randomly rotated samples of 70 texture classes obtained from the Brodatz and the VisTex albums. Comparative study results show that our method is highly efficient in rotation invariant texture classification.