Sparse Coding for Image Classification

In this article, I explore the implementation of sparse coding for image classification. Sparse coding is a dictionary-based signal processing technique that has found widespread applications in various fields. My research implements this technique through custom MATLAB programs I developed, accompanied by a demo for reference. The implementation involves key algorithms such as K-SVD for dictionary learning and Orthogonal Matching Pursuit (OMP) for sparse coefficient recovery. The demo showcases how sparse coding can be applied to image classification tasks, demonstrating the complete pipeline from feature extraction using learned dictionaries to classification with sparse representations. Through examining this approach, we can better understand the practical applications and advantages of sparse coding, including its ability to capture essential image features while maintaining computational efficiency. The MATLAB code includes functions for patch extraction, dictionary optimization, and classification metrics calculation. This exploration may provide insights into how sparse coding can be adapted to other domains beyond image processing. I hope this article proves helpful for your research and applications!