Efficient rotation- and scale-invariant texture classification method based on Gabor wavelets

Xudong Xie, Qionghai Dai, Kin-Man Lam, Hongya Zhao

Abstract: An efficient texture classification method is proposed that considers the effects of both the rotation and scale of texture images. In our method, the Gabor wavelets are adopted to extract local features of an image and the statistical properties of its gray-level intensities are used to represent the global features. Then, an adaptive, circular orientation normalization scheme is proposed to make the feature invariant to rotation, and an elastic cross-frequency searching mechanism is devised to reduce the effect of scaling. Our method is evaluated based on the Brodatz album and the Outex database, and the experimental results show that it outperforms the traditional algorithms.