LBP Algorithm Implementation for Image Texture Classification

This implementation uses the LBP (Local Binary Pattern) algorithm for image texture classification, based on the seminal paper: "Multiresolution Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns." The LBP algorithm is a widely-used method for texture feature extraction and classification in digital images. In the algorithm implementation, texture information is described by calculating Local Binary Patterns from image regions, where each pixel is compared with its neighbors to generate binary codes. Key implementation aspects include using circular neighborhoods with bilinear interpolation for rotation invariance, and multi-resolution analysis through varying radius parameters. The algorithm leverages grayscale invariance properties by focusing on relative intensity differences rather than absolute values. Code implementation typically involves steps like: preprocessing images, computing LBP histograms for different regions, applying uniform pattern mapping to reduce feature dimensionality, and using classifiers like SVM for texture categorization. Through this method, we can effectively analyze and classify textures within images, playing a significant role in computer vision and image processing applications such as material recognition, medical image analysis, and surface inspection systems.