Synthetic training samples for enhanced locality-constrained dictionary learning

Dictionary learning serves as a considerable role in image processing and pattern recognition. However, when applied to face classification, it may suffer from the issue of the limited quantity of training samples. Therefore, it becomes a challenge to obtain a robust and discriminative dictionary. Recently, locality-constrained and label embedding dictionary learning (LCLE-DL) takes the locality and label information of atoms into account to achieve an effective performance in image classification. In this study, the authors exploit a new approach which uses synthetic training samples to enhance this dictionary learning algorithm, so they name it STS-DL. Firstly, they strengthen the diversities of training samples by producing virtual samples. Secondly, the LCLE-DL algorithm is used to calculate two deviations on the basis of the original training samples and the authors’ newly synthetic samples, respectively. Finally, they integrate them together to perform the classification task, which produces a more promising performance for image recognition. Abundant experiments have been conducted on several benchmark databases, the experimental results illustrate that the proposed STS-DL shows a higher accuracy than the LCLE-DL method, as well as some state-of-the-art dictionary learning and sparse representation algorithms in image classification.