Spatially weighted order binary pattern for color texture classification Abstract   In this paper, we propose a novel descriptor called spatially weighted order binary pattern (SWOBP) for color texture classification. The SWOBP descriptor not only encodes color order information in different channels but also encodes color order relationships in the spatial domain. To achieve these goals, we introduce a color gradient channel to complement the traditional color channels and explore a multichannel color order pattern to jointly encode interchannel features. Furthermore, we decompose local color differences into spatially weighted binary templates and use them to encode color order information in a local neighborhood. Finally, we aggregate all the encoded features into image histograms as texture descriptor. Experiments on five benchmark databases demonstrate that the proposed SWOBP descriptor achieves the stateoftheart performance for color texture classification.

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.