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Object Class Recognition Using SNoW with a Part Vocabulary

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Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4482))

Abstract

In this paper we present a novel method for object class recognition. A vocabulary of object parts is automatically constructed from sample images of the object class by AdaBoost. Images are then represented using parts from this vocabulary. Based on this representation, the Sparse Network of Winnows (SNoW) learning architecture is employed to learn to recognize instances of the object class. Experimental results show that the method achieves high recognition accuracy on different data sets, and is highly robust to partial occlusion and background clutter.

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Author information

Authors and Affiliations

  1. Department of Automation, Tsinghua University, Beijing, 100084, P.R. China

    Ming Wen, Lu Wang, Lei Wang, Qing Zhuo & Wenyuan Wang

Authors
  1. Ming Wen
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  2. Lu Wang
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  3. Lei Wang
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  4. Qing Zhuo
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  5. Wenyuan Wang
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Engineering, York University, M3J 1P3, Toronto, Ontario, Canada

    Aijun An

  2. Institute of Computing Sciences, Poznań University of Technology, ul. Piotrowo 2, 60–965, Poznań, Poland

    Jerzy Stefanowski

  3. Department of Applied Computer Science, University of Winnipeg, R3B 2E9, Winnipeg, Manitoba, Canada

    Sheela Ramanna

  4. Department of Computer Science, University of Regina, S4S 0A2, Regina, Saskatchewan, Canada

    Cory J. Butz

  5. Department of Electrical and Computer Engineering, University of Alberta, T6G 2V4, Edmonton, Alberta, Canada

    Witold Pedrycz

  6. Institute of Compuer Science and Technology, Chongqing University of Posts and Telecommunications, 40065, Chongqing, P.R. China

    Guoyin Wang

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© 2007 Springer-Verlag Berlin Heidelberg

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Wen, M., Wang, L., Wang, L., Zhuo, Q., Wang, W. (2007). Object Class Recognition Using SNoW with a Part Vocabulary. In: An, A., Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W., Wang, G. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2007. Lecture Notes in Computer Science(), vol 4482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72530-5_63

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  • DOI: https://doi.org/10.1007/978-3-540-72530-5_63

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72529-9

  • Online ISBN: 978-3-540-72530-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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