Scale and rotation invariant Gabor texture descriptor for texture classification

Abstract

Scale and rotation invariant texture classification is a challenging and significant topic in texture analysis. This paper presents a scale and rotation invariant Gabor Texture Descriptor (GTD) for texture classification. Firstly, Gabor filters with different orientations and scales are carried out on an image. Secondly, to obtain a scale invariant GTD, different scale energies of these Gabor filtered images are calculated, and the optimal matching scales of the GTD are chosen by the sliding window filter with the scale energies. Thirdly, to extract a rotation invariant GTD, a new method using Discrete Fourier Transform is employed on the scale invariant GTD. At last, the distances between the GTDs of a query image and the target images are calculated for texture classification. Five different GTDs are employed for texture classification to demonstrate the effectiveness of the proposed scale and rotation invariant GTD.