LBP Feature Extraction with MATLAB Implementation

In this documentation, we will demonstrate how to implement a Local Binary Pattern (LBP) image feature extraction algorithm using MATLAB. LBP is a widely used texture descriptor that effectively captures local texture patterns in images. The algorithm works by comparing each pixel with its surrounding neighbors, generating binary codes that represent local texture variations. The MATLAB implementation includes key functions for: - Image preprocessing and grayscale conversion - LBP pattern calculation using radius and neighborhood parameters - Feature histogram generation for texture representation - Visualization of LBP patterns and extracted features By extracting LBP features, we obtain robust texture descriptors that can be utilized in various computer vision applications such as image classification, object recognition, and texture analysis. This program provides a fundamental understanding of LBP feature extraction principles and offers a practical reference implementation with commented code sections, parameter configuration options, and result visualization capabilities. The code structure follows modular design principles, separating image loading, LBP calculation, and feature aggregation into distinct functions for easy modification and extension. Users can adjust parameters like neighborhood size and sampling radius to optimize feature extraction for specific applications.