Self-optimized two phase test sample sparse representation method for image classification

Sparse Representation Classifiers and their variants are more and more used by computer vision and signal processing communities due to their good performance. Recently, it has been shown that the performance of Sparse Representation Classifiers and their variants in terms of accuracy and computational complexity can be enhanced by simply including a two-phase coding scheme regardless of the used coding scheme. The two-phase strategies use different schemes for selecting the examples that should be handed over to the next coding phase. However, all of them use a fixed and predefined number for these examples making the performance of the final classifier very dependent on this choice. This paper introduces three strategies for self-optimized size selection associated with Two Phase Test Sample Sparse Representation method. Experiments conducted on three face data sets show that the introduced scheme can outperform the classic two-phase strategies. Although the experiments were conducted on face data sets, the proposed schemes can be useful for a broad spectrum of pattern recognition problems.