SVM for Image Classification Using Block-Based Feature Extraction

SVM (Support Vector Machine) is an algorithm employed for image classification utilizing block-based feature extraction. It is primarily used for determining image categories, such as ancient architecture, water bodies, vegetation, and others. Through the application of the SVM algorithm, more precise and accurate image classification and recognition can be achieved. This algorithm finds extensive applications in computer vision and image processing domains and is recognized as a highly effective classification method.

Implementation typically involves dividing images into uniform blocks, extracting features like HOG (Histogram of Oriented Gradients) or texture descriptors from each block, and concatenating them into feature vectors. The SVM classifier then employs kernel functions (e.g., RBF or linear) to establish optimal hyperplanes for category separation. Key functions include svm.train() for model training and svm.predict() for classification, with parameters tuned via cross-validation to enhance accuracy.