LBP Operator: Algorithm for Image Texture Characterization

In computer vision, Local Binary Pattern (LBP) is an algorithm used for characterizing image texture. It operates by comparing the grayscale differences between a central pixel and its surrounding neighboring pixels, converting each pixel into a binary encoding pattern. This encoding methodology can be implemented through circular sampling with bilinear interpolation for rotation invariance, typically using 8 surrounding pixels (radius=1) or extended neighborhoods for multiscale analysis. The resulting LBP codes form histogram descriptors that effectively capture texture information. These descriptors find extensive applications in various domains including facial recognition (through uniform pattern classification), texture analysis, motion recognition, and image segmentation. Consequently, LBP feature descriptors are widely utilized in image processing and computer vision workflows, often serving as input features for machine learning classifiers like SVM or neural networks due to their computational efficiency and discriminant power.