Synthetic training samples for enhanced locality-constrained dictionary learning

The Journal of Engineering

Yanghao Zhang¹  Shaoning Zeng²  Wei Zeng³ 

[1]Electronics and Computer Science , University of Southampton , Southampton SO17 1BJ , UK

[2]School of Information Science and Technology , Huizhou University , No. 46 Avenue Yanda, Huizhou , People&apos

[3]s Republic of China

关键词: state-of-the-art dictionary learning;    dictionary learning algorithm;    image processing;    pattern recognition;    enhanced locality-constrained dictionary learning;    embedding dictionary;    image classification;    image recognition;    discriminative dictionary;   

DOI  :  10.1049/joe.2018.8311

学科分类：工程和技术（综合）

来源: IET

【 摘 要 】

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.

【 授权许可】

CC BY