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Texture Classification of the Entire Brodatz Database through an Orientational-Invariant Neural Architecture

  • F. J. Díaz-Pernas
  • M. Antón-Rodríguez
  • J. F. Díez-Higuera
  • M. Martínez-Zarzuela
  • D. González-Ortega
  • D. Boto-Giralda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5602)

Abstract

This paper presents a supervised neural architecture, called SOON, for texture classification. Multi-scale Gabor filtering is used to extract the textural features which shape the input to a neural classifier with orientation invariance properties in order to accomplish the classification. Three increasing complexity tests over the well-known Brodatz database are performed to quantify its behavior. The test simulations, including the entire texture album classification, show the stability and robustness of the SOON response.

Keywords

Little Square Support Vector Machine Training Rate Neural Architecture Texture Scene Texture Segregation 
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 2009

Authors and Affiliations

  • F. J. Díaz-Pernas
    • 1
  • M. Antón-Rodríguez
    • 1
  • J. F. Díez-Higuera
    • 1
  • M. Martínez-Zarzuela
    • 1
  • D. González-Ortega
    • 1
  • D. Boto-Giralda
    • 1
  1. 1.Higher School of Telecommunications EngineeringUniversity of ValladolidSpain

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