Embedded Sparse Representation For Image Classification

Image classification, such as face recognition and scene categorization, is an important research area in computer vision over the last decade. It has been successfully applied to many image analysis applications. Images usually have a large number of features, hence the dimensionality reduction methods are often employed before the subsequent classification to improve the classification results. A lot of methods have been proposed, including but not limited to PCA, ICA, LDA, and Bayesian Framework. Recently, compressive sensing and sparse learning have been widely studied and applied into computer vision research. Ma et al. suggested a new method called Sparse Representation Classification (SRC). This new framework provides new insights into two critical issues in image classification: feature extraction and robustness to occlusion. Motivated by this method, we proposed a new method called embedded sparse representation. This masterpiece combined the dimension reduction and classification into one. We proposed three possible objective functions and discussed the possible optimization ways to tackle them. During the optimization, we used Gibbs Optimization method to alternatively find the optimal subspace representation and the sparse weight factor. In the experiments, we verified the convergence of our method. Our method successfully extracted the subspace structure and got a better performance than SRC and other classical methods. It has also been shown that our method has the potential to be extended to other general high dimensional data. The possible improvement and future work have also been discussed.