SVM Classifier for Multi-Class Image Classification

In this implementation, we utilize a Support Vector Machine (SVM) classifier to perform multi-class image categorization. The SVM classifier is a widely-adopted machine learning algorithm that classifies images into distinct categories based on extracted features. Key implementation steps include: preprocessing images through resizing and normalization, extracting features using methods like HOG (Histogram of Oriented Gradients) or CNN-based features, and training the SVM model with appropriate kernel selection (linear, RBF, or polynomial). The classification process involves transforming raw pixel data into meaningful feature vectors using OpenCV or scikit-learn libraries, followed by model training with parameter optimization through grid search. This enables efficient organization of image datasets for applications such as image retrieval, pattern recognition, and computer vision systems. The SVM classifier demonstrates significant value in image processing workflows by providing high-accuracy classification with clear decision boundaries, making it particularly suitable for tasks requiring robust feature separation and scalable image taxonomy.