Neighborhood Estimated Local Binary Patterns for Texture Classification

Abstract

A novel texture classification approach based on neighborhood estimated local binary patterns (NELBP) is proposed. In the proposed approach, the local surrounding values of neighborhood estimated are introduced to operate binary patterns. Moreover, two different and complementary descriptors (average-based descriptor and differences-based descriptor) are extracted from local patches. Contrast experiments on Outex database and CUReT database demonstrate that the proposed NELBP is more robust to Gaussian noise than the conventional LBP for texture classification. In addition, the results also show that the combined complementary descriptor playes an important role in texture classification.