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Rotation-Invariant Texture Recognition

  • Javier A. Montoya-Zegarra
  • João P. Papa
  • Neucimar J. Leite
  • Ricardo da Silva Torres
  • Alexandre X. Falcão
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4842)

Abstract

This paper proposes a new texture classification system, which is distinguished by: (1) a new rotation-invariant image descriptor based on Steerable Pyramid Decomposition, and (2) by a novel multi-class recognition method based on Optimum Path Forest. By combining the discriminating power of our image descriptor and classifier, our system uses small size feature vectors to characterize texture images without compromising overall classification rates. State-of-the-art recognition results are further presented on the Brodatz dataset. High classification rates demonstrate the superiority of the proposed method.

Keywords

Recognition Accuracy Optimum Path Minimum Span Tree Texture Image Image Descriptor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Javier A. Montoya-Zegarra
    • 1
    • 2
  • João P. Papa
    • 2
  • Neucimar J. Leite
    • 2
  • Ricardo da Silva Torres
    • 2
  • Alexandre X. Falcão
    • 2
  1. 1.Computer Engineering Department, Faculty of Engineering, San Pablo Catholic University, Av. Salaverry 301, Vallecito, ArequipaPeru
  2. 2.Institute of Computing, State University of Campinas, Av. Albert Einstein 1216, Campinas, São PauloBrazil

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