A novel texture description by local multi-channels Gabor comprehensive patterns

Abstract

The texture description is an important research topic in the pattern recognition region. A new texture description algorithm using Local Multi-channels Gabor Comprehensive Patterns (LMGCP) was proposed. The motivation for the LMGCP model is to find more rich and discriminant texture measurement and deal with the high dimension problem of the local Gabor feature vector. First, the sample image was filtered by multi-orientation Gabor filters with multi-scale to extract their corresponding local Gabor magnitude map (LGMM). Second, the Local Comprehensive Patterns (LCP) were presented, which can compute the relationship between the referenced pixel and its neighbors by encoding gray-level difference based on 0°, 45°, 90°, 135°, 180°, 225°, 270°, 315° high orders direction derivatives patterns (DDP) and the direction magnitude patterns (DMP). Finally, the dimension of local Gabor feature vector is higher, so the LGMM were transformed by LCP named by LMGCP, which were concatenated into and feature vector. Simulated experiments and comparisons on subsets of Yale B and CMUPIE face databases under ideal condition, different illumination condition, different facial expression and partial occlusion show that the proposed algorithm is an outstanding method better than the LBP, the local derivative patterns, and the LTP, Local Gabor Transform.