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Group recursive discriminant subspace learning with image set decomposition

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Abstract

Discriminant subspace learning is a widely used feature extraction technique for image recognition, since it can extract effective discriminant features by employing the class information and Fisher criterion. A crucial research topic on this technique is how to rapidly extract sufficient and effective features. Recently, recursive discriminant subspace learning technique has attracted lots of research interest because it can acquire sufficient discriminant features. Generally, it recursively decomposes image samples and extracts features from a number of decomposed sample sets. The major drawback of most recursive discriminant subspace learning methods is that they calculate the projective vectors one by one, such that they suffer from big computational costs. The recursive modified linear discriminant method and the incremental recursive Fisher linear discriminant method employ a simple solution for this problem, which calculates the class number minus one projective vectors in each recursion. However, this solution produces the unfavorable projective vectors with poor discriminant capabilities, and it cannot provide the terminating criterion for recursive computation and make the projective vectors orthogonal. In this paper, we propose a novel recursive learning approach that is group recursive discriminant subspace learning, which can rapidly learn multiple orthogonal subspaces with each spanned by a group of projective vectors. And we present a rule to select favorable projective vectors per recursion and provide a matrix-form-based terminating criterion to determine the number of recursions. Experiments on three widely used databases demonstrate the effectiveness and efficiency of the proposed approach.

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Acknowledgments

The work described in this paper was fully supported by the National Natural Science Foundation of China under Project Nos. 61272273 and 61233011, the Major Science and Technology Innovation Plan of Hubei Province under Project No. 2013AAA020, the Research Project of Nanjing University of Posts and Telecommunications under Project No. XJKY14016, and the Postgraduate Scientific Research and Innovation Plan of Jiangsu Province Universities under Project No. CXLX13_465.

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Authors and Affiliations

  1. College of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China

    Fei Wu

  2. College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China

    Xiao-Yuan Jing, Yong-Fang Yao & Dong Yue

  3. State Key Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan, 430072, China

    Xiao-Yuan Jing

  4. Institute of Advanced Technology and College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China

    Dong Yue

  5. School of Computer, Wuhan University, Wuhan, 430072, China

    Jun Chen

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  1. Fei Wu
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  2. Xiao-Yuan Jing
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  3. Yong-Fang Yao
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  4. Dong Yue
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  5. Jun Chen
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Corresponding author

Correspondence to Xiao-Yuan Jing.

Appendix

Appendix

Figures 8 and 9 show the approximate sample sets and the corresponding difference sample sets on the extended YaleB face and HK PolyU palmprint databases, respectively.

Fig. 8
figure 8

Approximate images and difference images of the first 8 stages on extended YaleB database

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Fig. 9
figure 9

Approximate images and difference images of the first 8 stages on HK PolyU palmprint database

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Wu, F., Jing, XY., Yao, YF. et al. Group recursive discriminant subspace learning with image set decomposition. Neural Comput & Applic 27, 1693–1706 (2016). https://doi.org/10.1007/s00521-015-1966-0

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  • DOI: https://doi.org/10.1007/s00521-015-1966-0

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