MATLAB HOG Feature Extraction and SVM Classification Tutorial

HOG (Histogram of Oriented Gradients) is a widely used feature descriptor in image processing and computer vision, particularly effective for object detection and recognition tasks. In MATLAB, we can combine HOG features with SVM (Support Vector Machine) classifiers to build a robust image recognition system. The HOG feature extraction process involves calculating gradient orientations for each pixel in an image and statistically distributing them into orientation bins to form histograms. MATLAB provides the built-in `extractHOGFeatures` function, which efficiently computes HOG descriptors from input images. This function accepts parameters like cell size, number of bins, and block size to optimize feature extraction for specific applications. For classification training using SVM, MATLAB offers the `fitcsvm` function, which implements supervised learning for classification problems. The function takes HOG features from training data along with their corresponding labels, and learns to differentiate between various objects or categories. Key parameters include kernel type (linear, polynomial, or RBF), box constraint, and kernel scale, which can be optimized using cross-validation techniques. During the testing phase, HOG features are extracted from test images using the same `extractHOGFeatures` function configuration. The trained SVM model then performs classification predictions through MATLAB's `predict` function, which returns class labels and confidence scores for each test sample. This HOG+SVM combination is suitable for various computer vision applications including pedestrian detection, gesture recognition, and object classification. The approach offers significant advantages: HOG features provide robustness to illumination changes and pose variations, while SVM delivers excellent performance for small-sample classification problems. The implementation typically involves preprocessing steps like image resizing, gradient computation using Sobel filters, and feature normalization to ensure consistent performance across different datasets.