Local Binary Patterns versus Signal Processing Texture Analysis

Traditionally texture analysis is approached either by statistically evaluating the distribution of the pixels in a local neighbourhood or by filtering the image with a bank of filters that are applied to capture the changes in the spatial/frequency domain. The aim of this paper is to review and provide a detailed performance evaluation of a number of texture descriptors that analyse texture at micro-level such as Local Binary Patterns (LBP) and a number of standard filtering techniques that sample the texture information using either a bank of isotropic filters or Gabor filters. The experimental tests were conducted on standard databases where the classification results are obtained for single and multiple texture orientations. In our study we have also analysed the performance of standard filtering texture analysis techniques (such as those based of LM and MR8 filter banks) when applied to the classification of texture images contained in standard Outex and Brodatz databases.