Comprehensive Documentation on LBP Basic Algorithm and Its Variants

In-depth Analysis of LBP Basic Algorithm and Variation Implementations

LBP (Local Binary Pattern) is a classical algorithm for image texture feature extraction. Its core concept involves comparing pixel values between a central pixel and its neighboring pixels to generate binary descriptors that characterize local texture features.

Basic LBP Algorithm Workflow: For each pixel in the image, select its 3×3 neighborhood Use the center pixel as threshold to binarize surrounding 8 pixels' gray values (set to 1 if ≥ threshold, otherwise 0) Combine these binary bits in fixed direction (typically clockwise) to form an 8-bit number Convert the binary number to decimal as the LBP value for the center point

Common Improved Algorithms: Circular LBP: Uses circular neighborhoods instead of square ones, handling non-integer coordinate points through bilinear interpolation Rotation-Invariant LBP: Records minimum LBP values under different rotation angles to eliminate rotational effects Uniform Pattern LBP: Counts binary string transition times, classifying patterns with more than 2 transitions into the same category Multi-Scale LBP: Combines circular neighborhoods with different radii to obtain multi-scale texture features

MATLAB Implementation Key Points: Utilize matrix operations to optimize computational efficiency Handle image boundary pixels through padding techniques Employ histogram statistics for feature representation Use visualization functions to display texture feature distributions

Application Scenarios: Texture feature extraction in face recognition systems Industrial surface defect detection Medical image analysis Scene classification tasks

The algorithm's advantages include computational simplicity and certain robustness to illumination changes, while improved versions further enhance rotation adaptability and feature discriminative power. MATLAB's matrix operation capabilities make it an ideal tool for implementing and validating LBP algorithms, particularly through efficient vectorization techniques that avoid looping over individual pixels.