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Rotation-invariant texture classification using a two-stage wavelet packet feature approach

A novel two-stage wavelet packet feature approach for classification of rotated textured images is discussed. In the first stage, a set of sorted and dominant wavelet packet features is extracted from a texture image and a Mahalanobis distance classifier is employed to output N best classes. In the second stage, another set of wavelet packet features is extracted from the polarised form of the sample texture image and the most dominant wavelet packet features are selected and passed to the radial basis function (RBF) classifier with the N best classes to output the final matched class. Experimental results, based on a large sample data set of twenty distinct natural textures selected from the Brodatz album with different orientations, show that the proposed method outperforms the similar wavelet methods and the other rotation invariant texture classification schemes, and an overall accuracy rate of 91.4% was achieved.