Multi-resolution local Gabor wavelets binary patterns for gray-scale texture description

Abstract

In this letter an effective multi-resolution and rotation-invariant texture description approach is presented, which can be utilized in various computer vision and image processing tasks such as texture classification and texture segmentation. In the proposed method, a given gray-scale texture image is first processed by a bank of Gabor wavelets (filters) at different scales and orientations. The obtained filters’ responses are then further processed, and a set of local binary patterns called “Local Gabor Wavelets Binary Patterns” (LGWBPs) are computed by comparing the local filters’ outputs at different orientations with the global mean of filters’ outputs at the same orientations. The obtained patterns are then converted to a number of decimal rotation-invariant codes, and a histogram of the resultant codes at different scales is finally used as a texture feature vector. Experimental results on three popular texture datasets (Outex TC10, CUReT, and Brodatz) indicate that the proposed method achieves high texture classification accuracy, especially in the presence of various levels of Gaussian noise.