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      Synthetic training samples for enhanced locality-constrained dictionary learning


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          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.

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              A Locality-Constrained and Label Embedding Dictionary Learning Algorithm for Image Classification.

              Locality and label information of training samples play an important role in image classification. However, previous dictionary learning algorithms do not take the locality and label information of atoms into account together in the learning process, and thus their performance is limited. In this paper, a discriminative dictionary learning algorithm, called the locality-constrained and label embedding dictionary learning (LCLE-DL) algorithm, was proposed for image classification. First, the locality information was preserved using the graph Laplacian matrix of the learned dictionary instead of the conventional one derived from the training samples. Then, the label embedding term was constructed using the label information of atoms instead of the classification error term, which contained discriminating information of the learned dictionary. The optimal coding coefficients derived by the locality-based and label-based reconstruction were effective for image classification. Experimental results demonstrated that the LCLE-DL algorithm can achieve better performance than some state-of-the-art algorithms.

                Author and article information

                The Journal of Engineering
                J. Eng.
                The Institution of Engineering and Technology
                28 August 2018
                25 September 2018
                November 2018
                : 2018
                : 16
                : 1761-1767
                [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's Republic of China
                JOE.2018.8311 JOE.2018.8311.R1

                This is an open access article published by the IET under the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/3.0/)

                The 2nd 2018 Asian Conference on Artificial Intelligence Technology (ACAIT 2018)
                Chongqing University of Technology, China
                8–10 June 2018
                : 18 July 2018
                : 17 August 2018
                : 20 August 2018
                Page count
                Pages: 0
                The 2nd 2018 Asian Conference on Artificial Intelligence Technology (ACAIT 2018)

                Software engineering,Data structures & Algorithms,Robotics,Networking & Internet architecture,Artificial intelligence,Human-computer-interaction
                discriminative dictionary,state-of-the-art dictionary learning,image classification,image recognition,embedding dictionary,dictionary learning algorithm,enhanced locality-constrained dictionary learning,pattern recognition,learning (artificial intelligence),image processing,image representation


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