An efficient texture classification algorithm using Gabor wavelet

In this paper we have investigated the application of nonseparable Gabor wavelet transform for texture classification. We have compared the effect of applying the dyadic wavelet transform as a traditional method with Gabor wavelet for texture extraction. It is well known that Gabor wavelets attain maximum joint space-frequency resolution which is highly significant in the process of texture extraction in which the conflicting objectives of accuracy in texture representation and texture spatial localization are both important. This fact has been explored in our results as they show that the classification rate obtained for Gabor wavelet is higher that those obtained using dyadic wavelets. Based on our experiments, the Gabor wavelet is more appropriate than dyadic wavelets for texture classification as it leads to a better discrimination of textures.