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Compressed Sensing Ensemble Classifier for Human Detection

  • Conference paper
Book cover Intelligence Science and Big Data Engineering (IScIDE 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8261))

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Abstract

This paper proposes a novel Compressed Sensing Ensemble Classifier (CSEC) for human detection. The proposed CSEC employs the compressed sensing technique to get a more sparse model with a more reasonable selection of base classifiers. The major contributions of this paper are: 1) a novel principled framework for ensemble classifier design based on compressed sensing; 2) a new concept of considering both the simplicity of ensemble classifier and irrelevance of base classifiers towards optimal classifier design; and 3) a quadratic function for CSEC optimization which includes a new optimizable positive semi-definite relevance matrix to simultaneously select appropriate base classifiers with minimized relevance. Experimental results on INRIA and SDL databases show that the performance of CSEC is better than two most popular classifiers SVM and AdaBoost, as well as a most recent method CLML.

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Author information

Authors and Affiliations

  1. Science and Technology on Aircraft Control Laboratory, School of Automation Science and Electrical Engineering, BeiHang University, Beijing, 100191, China

    Baochang Zhang & Juan Liu

  2. School of Engineering, Griffith University, Australia

    Yongsheng Gao

  3. Shenzhen Key Lab for Computer Vision and Pattern Recognition, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China

    Jianzhuang Liu

  4. Department of Information Engineering, The Chinese University of Hong Kong, China

    Jianzhuang Liu

Authors
  1. Baochang Zhang
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  2. Juan Liu
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  3. Yongsheng Gao
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  4. Jianzhuang Liu
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Editor information

Editors and Affiliations

  1. School of Automation and Electrical Engineering, University of Science and Technology, Xueyuan Road No. 30, 100083, Beijing, China

    Changyin Sun

  2. Department of Psychology, Peking University, Yiheyuan Road No. 5, 100871, Beijing, China

    Fang Fang

  3. Department of Computer Science and Technology, Nanjing University, Xianlin Avenue No. 163, 210023, Nanjing, China

    Zhi-Hua Zhou

  4. School of Automation, Southeast University, Sipailou No. 2, 210096, Nanjing, China

    Wankou Yang

  5. Institute of Automation, Chinese Academy of Sciences, No. 95 East Zhongguancun Road, 100190, Beijing, China

    Zhi-Yong Liu

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© 2013 Springer-Verlag Berlin Heidelberg

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Zhang, B., Liu, J., Gao, Y., Liu, J. (2013). Compressed Sensing Ensemble Classifier for Human Detection. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_106

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  • DOI: https://doi.org/10.1007/978-3-642-42057-3_106

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-42056-6

  • Online ISBN: 978-3-642-42057-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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