Texture classification using rotated wavelet filters

We propose an approach to the texture classification problem using a set of two-dimensional (2-D) wavelet filters that are nonseparable and oriented for improved characterization of diagonally oriented textures. Channel energies are estimated at the output of both the new filter bank and a standard discrete wavelet frames (DWF) filter bank. Classification results obtained using each individual method and in combination are presented. The results show that the oriented filter set results in finer discrimination providing complementary texture information to the DWF by making use of its orientation selectivity. As a result, a combination of the features from the output of two filter banks improved the classification accuracy significantly with a smaller number of features.