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International Conference on Brain Inspired Cognitive Systems

BICS 2016: Advances in Brain Inspired Cognitive Systems pp 321–330Cite as

Low-Rank Image Set Representation and Classification

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10023))

Abstract

Image set representation and classification is an important problem in computer vision and pattern recognition area. In real application, image set data often come with kinds of noises, corruptions or large errors which usually make the recognition/learning tasks of image set more challengeable. In this paper, we utilize the low-rank representation/component of image set to represent the observed image set which is called Low-rank Image Set Representation (LRISR). Comparing with original observed image set, LRISR is generally noiseless and thus can encourage more robust learning process. Based on LRISR, we then use covariate-relation graph to encode the geometric relationship between covariates/features of LRISR and thus extract description vectors for LRISR classification task. Experimental results on several datasets demonstrate the benefits of the proposed image set representation and classification method.

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Acknowledgments

This work was supported by the National Nature Science Foundation of China (61602001,61671018,61472002); Natural Science Foundation of Anhui Higher Education Institutions of China (KJ2016A020), Natural science foundation of Anhui Province (1508085QF127); The Open Projects Program of National Laboratory of Pattern Recognition.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Anhui University, No.111 Jiulong Road, Hefei, China

    Youxia Cao, Bo Jiang, Zhuqiang Chen, Jin Tang & Bin Luo

Authors
  1. Youxia Cao
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  2. Bo Jiang
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  3. Zhuqiang Chen
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  4. Jin Tang
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  5. Bin Luo
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Corresponding author

Correspondence to Bo Jiang .

Editor information

Editors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  2. Computing Science and Mathematics, University of Stirling, Stirling, United Kingdom

    Amir Hussain

  3. Anhui University, Anhui, China

    Bin Luo

  4. National University of Singapore, Singapore, Singapore

    Kay Chen Tan

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Yi Zeng

  6. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhaoxiang Zhang

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© 2016 Springer International Publishing AG

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Cao, Y., Jiang, B., Chen, Z., Tang, J., Luo, B. (2016). Low-Rank Image Set Representation and Classification. In: Liu, CL., Hussain, A., Luo, B., Tan, K., Zeng, Y., Zhang, Z. (eds) Advances in Brain Inspired Cognitive Systems. BICS 2016. Lecture Notes in Computer Science(), vol 10023. Springer, Cham. https://doi.org/10.1007/978-3-319-49685-6_29

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  • DOI: https://doi.org/10.1007/978-3-319-49685-6_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49684-9

  • Online ISBN: 978-3-319-49685-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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