Combining Complex Networks and Multi-level Local Binary Patterns for Texture Analysis

This study presents a novel approach to texture analysis that combines Complex Network Texture Descriptors (CNTD) with the Local Binary Pattern (LBP) technique. By generating "pattern images" through the application of LBP to the original texture image, the method introduces new sources of information that are further explored via CNTD method. We employed Particle Swarm Optimization (PSO) to investigate various combinations of these LBP patterns and descriptors and compared our results across three benchmark datasets. The proposed approach achieved accuracy rates of 99.54%, 99.88%, and 97.86% on the Vistex, Brodatz, and USPTex databases, respectively. These results highlight the effectiveness of the hybrid strategy in producing a highly discriminative feature vector for robust texture classification.