Noise-robust statistical feature distributions for texture analysis

A novel image feature extraction methodology is proposed in this study. By incorporating fuzzy logic into the well- established Local Binary Pattern (LBP) approach we derive statistical feature distributions suitable for noise-robust tex- ture representation. The proposed Fuzzy Local Binary Pat- tern (FLBP) approach is based on the assumption that a local image neighbourhood may be characterized by more than a single binary pattern. The effectiveness of the pro- posed methodology is demonstrated by classification ex- periments on noise degraded Brodatz textures. The classifi- cation performance obtained with the FLBP features was higher than the one obtained with the original LBP features for various noise levels.