A Rotation and Scale Invariant Texture Description Approach This paper presents a novel texture description approach, which is robust to variances in rotation, scale and illumination in images, to classify the texture of images. A limitation with traditional methods is that they are more or less sensitive to the mentioned changes in images. To overcome this problem, we propose a novel Local Haar Binary Pattern (LHBP) based framework to ensure invariance in global rotation, scale, and light change. Our method consists of two components: feature extraction and scale selfadaptive classification. The global rotation invariant LHBP histogram features are extracted against the variances of illumination and global rotation, and the scale selfadaptive strategy is used for optimizing the classification of different scale textures. Evaluation results on Outex and Brodatz databases illustrate the significant advantages of the proposed approach over existing algorithms.

Scale and Rotation Invariant Gabor Texture Descriptor for Texture Classification 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.