Rotation invariant texture classification based on a directional filter bank

This paper presents a rotation invariant texture classification method using a special directional filter bank (DFB). The new method extracts a set of coefficient vectors from the directional subband domain, and models them with multivariate Gaussian density. Eigen-analysis is then applied to the covariance metrics of these density functions to form rotation invariant feature vectors. Classification is based on the distance between known and unknown feature vectors. Two distance measures are studied in this work, including the Kullback-Leibler distance and the Euclidean distance. Experimental results have shown that this DFB is very effective in capturing directional information of texture images, and the proposed rotation invariant feature generation and classification method can in fact achieve high classification accuracy on both non-rotated and rotated images