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International Conference Image Analysis and Recognition

ICIAR 2005: Image Analysis and Recognition pp 746–753Cite as

Rotation-Invariant Texture Classification Using Steerable Gabor Filter Bank

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3656))

Abstract

An efficient rotation invariant feature extraction technique for texture classification based on Gabor multi-channel filtering is proposed. In this technique, Gabor function is approximated by a set of steerable basis functions, which results in a significant saving in the computation cost. The classification of 15 classes of Brodatz textures are considered in our experiments. Results show that up to 40% of computation can be saved compared with traditional Gabor multi-channel filtering method. In the mean time, almost the same high texture classification correct rate can be achieved.

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Author information

Authors and Affiliations

  1. Dept. Computer Science and Software Engineering, Concordia University, 1455 De Maisonneuve Blvd. West, Montreal, Quebec, H3G 1M8, Canada

    Wumo Pan & T. D. Bui

  2. Centre for Pattern Recognition and Machine Intelligence, Concordia University, 1455 de Maisonneuve Blvd. West, Suite GM-606, Montréal, Québec, H3G 1M8, Canada

    Wumo Pan & C. Y. Suen

Authors
  1. Wumo Pan
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  2. T. D. Bui
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  3. C. Y. Suen
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Editor information

Editors and Affiliations

  1. Electrical and Computer Engineering Department, University of Waterloo,  

    Mohamed Kamel

  2. FEUP - Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, P.O. Box, 4200-465, Porto, Portugal

    Aurélio Campilho

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© 2005 Springer-Verlag Berlin Heidelberg

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Pan, W., Bui, T.D., Suen, C.Y. (2005). Rotation-Invariant Texture Classification Using Steerable Gabor Filter Bank. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2005. Lecture Notes in Computer Science, vol 3656. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11559573_91

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  • DOI: https://doi.org/10.1007/11559573_91

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29069-8

  • Online ISBN: 978-3-540-31938-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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