MATLAB Code Implementation for Extracting Gabor Features, LBP Features, and LGBP Features

The process of extracting Gabor features, LBP features, and LGBP features involves sophisticated image processing and computer vision techniques. For Gabor feature extraction, implementation typically requires creating a bank of Gabor filters with multiple orientations and scales, followed by convolving these filters with the input image. The LBP algorithm involves comparing each pixel with its neighbors to generate binary patterns that encode texture information. LGBP combines Gabor filtering with LBP by first applying Gabor filters and then computing LBP features on the filtered responses. When implementing these feature extraction techniques in MATLAB, key functions like gaborFilterBank for creating Gabor filters, efficient convolution operations using imfilter or conv2, and optimized LBP computation through neighborhood pixel comparison are essential. The code should include parameter optimization for filter scales, orientations, and neighborhood sizes to balance feature discrimination power and computational efficiency. For practical implementation, consider creating modular functions that handle each feature type separately, with clear input/output parameters and proper documentation. Runtime optimization can be achieved through vectorized operations and precomputation of filter banks. Validation should include testing on standard datasets and comparing performance metrics like feature discrimination accuracy and computational time across different parameter configurations. These features are widely applicable in computer vision tasks such as facial recognition systems (where Gabor features capture facial textures), texture classification (using LBP patterns), and robust feature representation (through LGBP's combined approach). Proper implementation requires thorough testing on target applications to ensure optimal parameter selection and feature effectiveness.