Classifying rotated textures using wavelet packet signatures

This paper proposes a novel approach to the classification of rotated texture images. The proposed classification method involves decomposing a texture image with a family of real orthonormal wavelet bases for different levels, computing the wavelet packet coefficients, and computing the energy signatures using the wavelet packet coefficients. Such energy signatures are sorted and used as a feature for texture image classification. We employ a Mahalanobis distance classifier to classify a set of twenty distinct natural textures selected from the Brodatz album. Experimental results, based on a large sample data set having different orientations, show that the proposed method outperforms other methods which may perform well in the classification of texture images having the same orientation