Image Classification Algorithm Based on Locality-Constrained Linear Coding

This MATLAB source code provides a detailed implementation of an image classification algorithm based on Locality-Constrained Linear Coding (LLC), which is a widely used method in computer vision applications. The algorithm effectively extracts discriminative features from images through locality-constrained linear coding and utilizes these features for classification tasks. The core methodology involves using the LLC model to create sparse representations of images, followed by classifier application to categorize the encoded results. Key implementation aspects include: - Local feature extraction using dense sampling or keypoint detection - Codebook generation through k-means clustering - Locality-constrained sparse coding with regularization parameters - Feature pooling operations (spatial pyramid matching) - SVM classifier integration with kernel functions The algorithm achieves accurate image classification with superior performance metrics. It demonstrates significant robustness, maintaining effective classification even under noisy conditions or image distortions. The implementation includes parameter optimization routines and cross-validation modules for performance evaluation. This approach shows promising application potential in various image classification domains including object recognition, scene classification, and visual content analysis.