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Bark Classification Based on Contourlet Filter Features Using RBPNN

  • Zhi-Kai Huang
  • Zhong-Hua Quan
  • Ji-Xiang Du
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)

Abstract

This paper proposed a new method of extracting texture features based on contourlet domain in RGB color space. In addition, the application of these features for bark classification applying radial basis probabilistic network (RBPNN) has been introduced. In this method, the bark texture feature is firstly extracted by decomposing an image into 6 subbands using the 7-9 biorthogonal Debauches wavelet transform, where each subband is fed to the directional filter banks stage with 32 directions at the finest level, then the mean and standard deviation of the image output are computed. The obtained feature vectors are fed up into RBPNN for classification. Experimental results show that, features extracted using the proposed approach can be more efficient for bark texture classification than gray bark image.

Keywords

Hide Layer Radial Basis Function Neural Network Average Recognition Rate Extract Texture Feature Directional Filter Bank 
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 2006

Authors and Affiliations

  • Zhi-Kai Huang
    • 1
    • 2
  • Zhong-Hua Quan
    • 1
    • 2
  • Ji-Xiang Du
    • 1
    • 2
  1. 1.Intelligent Computing Lab, Hefei Institute of Intelligent MachinesChinese Academy of SciencesHefeiChina
  2. 2.Department of AutomationUniversity of Science and Technology of China 

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