MATLAB Implementation of HOG Feature Extraction

We employed the HOG (Histogram of Oriented Gradients) feature extraction method to analyze our test dataset. The algorithm implementation involves calculating gradient information from images, dividing them into small connected cells, and computing histograms of gradient directions within each cell. Our MATLAB implementation utilizes key functions like gradient computation, spatial binning, and block normalization to enhance feature discrimination. The method processes images through several computational stages: first computing horizontal and vertical gradients using convolution kernels, then calculating gradient magnitudes and orientations for each pixel. The image is divided into cells (typically 8x8 pixels), where gradient directions are accumulated into orientation bins. These cell histograms are then grouped into larger blocks with overlapping regions, applying L2-norm normalization to improve illumination invariance. This approach enables more accurate object recognition by capturing local shape characteristics while maintaining robustness to lighting variations. The provided code demonstrates practical implementation details including parameter optimization for cell size, number of orientation bins, and block normalization strategies. We hope this implementation helps developers better understand and apply HOG features in their computer vision projects, particularly for tasks like pedestrian detection and object recognition.