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Improving sparse representation-based image classification using truncated total least squares

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Abstract

In the field of face recognition, the Sparse Representation-based Classification (SRC) method can effectively deal with many common problems such as face occlusion, lighting and expression changes. Truncated Total Least Squares (TTLS) method is determined by using an optimal cutoff factor k. The choice of truncation factor regularization parameters directly affects the quality of the solution. The truncation of total least squares is suitable for solving the linear problems of the same kind. According to this motivation, a classification method TSRC based on the truncation representation is proposed. The truncated global least squares regularization is fused with sparse representation to optimize the representation coefficients. We performed a large number of experiments with this method on several popular benchmark datasets, and the results showed that the sparse representation can be improved if it is combined with the truncation. In most cases, the proposed truncation-based classification method has higher classification accuracy.

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Acknowledgements

The work is supported by the national social science fund in China (Grant No.15BTJ024), the planning fund project of humanities and social science research in Chinese Ministry of Education (Grant No.14YJAZH040), the teaching reform of higher education in undergraduate colleges and universities from Guangdong province (Grant No. [2016]236-502, [2015]173-588), Huizhou Municipal Science and Technology Project Fund (Grant No. 2016X0422037, 2017C0405021), the Field Scientific Research Project of Huizhou University (Grant No.hzu201716), Indigenous Innovation’s Capability Development Program of Huizhou University (Grant No. hzu201815), Guangdong Provincial Key Laboratory of Technology and Finance & Big Data Analysis(Grant No.2017B030301010), Guangdong Key Research Base of Technology and Finance(Grant No.2014B030303005), Platform of Credit Financing and Trade for Guangdong Technological Enterprises(Grant No.2014B080807035).

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  1. School of Information Science and Technology, Huizhou University, Huizhou, 516007, Guangdong, China

    Hui Li, Hui Jiang, Huabin Wang & Wei Zeng

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  1. Hui Li
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Correspondence to Hui Jiang.

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Li, H., Jiang, H., Wang, H. et al. Improving sparse representation-based image classification using truncated total least squares. Multimed Tools Appl 78, 12007–12026 (2019). https://doi.org/10.1007/s11042-018-6740-8

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  • DOI: https://doi.org/10.1007/s11042-018-6740-8

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