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Synthesis K-SVD based analysis dictionary learning for pattern classification

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Abstract

In the fields of computer vision and pattern recognition, dictionary learning techniques have been widely applied. In classification tasks, synthesis dictionary learning is usually time-consuming during the classification stage because of the sparse reconstruction procedure. Analysis dictionary learning, which is another research line, is more favorable due to its flexible representative ability and low classification complexity. In this paper, we propose a novel discriminative analysis dictionary learning method to enhance classification performance. Particularly, we incorporate a linear classifier and the supervised information into the traditional analysis dictionary learning framework by adding a discrimination error term. A synthesis K-SVD based algorithm which can effectively constrain the sparsity is presented to solve the proposed model. Extensive comparison experiments on benchmark databases validate the satisfactory performance of our method.

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Notes

  1. http://www.umiacs.umd.edu/~zhuolin/projectlcksvd.html.

  2. http://www.cse.buffalo.edu/~jcorso/r/actionbank.

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Acknowledgements

This work is funded by the National Natural Science Foundation of China (Grant No. 61402079), the Foundation for Innovative Research Groups of the NSFC (Grant No. 71421001), the Open Project Program of the National Laboratory of Pattern Recognition (NLPR, No. 201600022).

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Authors and Affiliations

  1. School of Information and Communication Engineering, Dalian University of Technology, Dalian, 116024, China

    Qianyu Wang, Yanqing Guo & Xiangwei Kong

  2. Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, 518055, China

    Jun Guo

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  1. Qianyu Wang
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  2. Yanqing Guo
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  3. Jun Guo
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  4. Xiangwei Kong
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Correspondence to Yanqing Guo.

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Wang, Q., Guo, Y., Guo, J. et al. Synthesis K-SVD based analysis dictionary learning for pattern classification. Multimed Tools Appl 77, 17023–17041 (2018). https://doi.org/10.1007/s11042-017-5269-6

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  • DOI: https://doi.org/10.1007/s11042-017-5269-6

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