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A Matrix-Oriented Method for Appearance-Based Data Compression – An Idea from Group Representation Theory

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Advances in Biometric Person Authentication (SINOBIOMETRICS 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3338))

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Abstract

Motivated by ideas of group representation theory, we propose a matrix-oriented method to dimension reduction for image data. By virtue of the action of Stiefel manifold, the original image representations can be directly contracted into a rather low-dimensional space. Experimental results show that the performance of PCA and LDA is significantly enhanced in the transformed space. In addition, the reconstructed images by proposed algorithm are better than those by 2DPCA.

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References

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Author information

Authors and Affiliations

  1. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200030, China

    Deli Zhao & Chongqing Liu

  2. Department of Mathematics, Shanghai Jiao Tong University, Shanghai, 200030, China

    Yuehui Zhang

Authors
  1. Deli Zhao
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  2. Chongqing Liu
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  3. Yuehui Zhang
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Editor information

Editors and Affiliations

  1. Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences,  

    Stan Z. Li

  2. Department of Electronics & Communication Engineering, Sun Yat-Sen University, Guangzhou, China

    Jianhuang Lai

  3. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  4. Center of Computer Vision, School of Mathematics and Computing Science, Sun Yat-sen University, 510275, Guangzhou, China

    Guocan Feng

  5. School of Computer Science and Engineering, Beihang University, Beijing, China

    Yunhong Wang

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© 2004 Springer-Verlag Berlin Heidelberg

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Cite this paper

Zhao, D., Liu, C., Zhang, Y. (2004). A Matrix-Oriented Method for Appearance-Based Data Compression – An Idea from Group Representation Theory. In: Li, S.Z., Lai, J., Tan, T., Feng, G., Wang, Y. (eds) Advances in Biometric Person Authentication. SINOBIOMETRICS 2004. Lecture Notes in Computer Science, vol 3338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30548-4_45

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  • DOI: https://doi.org/10.1007/978-3-540-30548-4_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24029-7

  • Online ISBN: 978-3-540-30548-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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