Rotation invariant texture classification using Gabor wavelets

This paper presents a new method for rotation invariant texture classification based on Gabor wavelets. The Gabor representation has been shown to be optimal in the sense of mimimizing the joint two-dimensional uncertainty in space and frequency, and the Gabor wavelet can be used to decompose an image into multiple scales and multiple orientations. Two group features, i.e. the global feature vector and local feature matrix, can be constructed by the mean and variance of the Gabor filtered image. The global feature vector is rotation invariant, and the local feature matrix can be adjusted by a circular shift operation to rotation invariant so that all images have the same dominant direction. By the two group features, a discriminant can be found to classify the rotated images. In the primary experiments, comparatively high correct classification rates were obtained using a large sample sets with 1998 rotated images of 111 Brodazt texture classes.