Semi-Supervised Image Classification Using Support Vector Machines

This implementation employs Support Vector Machines (SVM) for semi-supervised image classification. Following the methodology from reference [1], we perform supervised feature learning using all available data—including both labeled and unlabeled samples. The algorithm capitalizes on images' rich categorical attributes to automatically generate prototype sets from existing data. For each prototype set, feature learning is conducted using techniques like convolutional neural networks or handcrafted feature extractors. The learned features are then projected and concatenated through dimensionality reduction methods (e.g., PCA or autoencoders) to form comprehensive image representations for classification.

Compared to traditional semi-supervised approaches that primarily rely on unlabeled data to define classification boundaries (e.g., Transductive SVM or graph-based methods), our method achieves higher classification accuracy by leveraging richer feature representations. The key implementation steps include: 1) Prototype set generation using clustering algorithms (K-means or hierarchical clustering), 2) Feature extraction per prototype set using CNN architectures like ResNet or VGG, 3) Feature projection via linear discriminant analysis or manifold learning, and 4) SVM classification with kernel functions (RBF or polynomial) for final decision boundaries. This approach demonstrates practical significance for computer vision and pattern recognition applications by effectively utilizing unlabeled data to enhance model generalization.

The technical implementation typically involves Python libraries such as scikit-learn for SVM optimization, OpenCV for image preprocessing, and TensorFlow/PyTorch for deep feature extraction. Hyperparameter tuning for SVM (C parameter, kernel selection) and prototype set size optimization are critical for performance improvement.