Texture Classification Via Stationary-Wavelet Based Contourlet Transform

  • Ying Hu
  • Biao Hou
  • Shuang Wang
  • Licheng Jiao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4153)


A directional multiresolution approach was proposed for texture analysis and classification based on a modified contourlet transform named the stationary wavelet-based contourlet transform (SWBCT). In the phase for extracting features after the decomposition, energy measures, Hu moments and co-occurrence matrices were calculated respectively. The progressive texture classification algorithm had better performance compared with several other methods using wavelet, stationary wavelet, brushlet, contourlet and Gabor filters. Moreover, in the case that there are only small scale samples for training, our method can also obtain a satisfactory result.


Texture Analysis Energy Measure Decomposition Level Invariant Moment Directional Decomposition 
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

  • Ying Hu
    • 1
  • Biao Hou
    • 1
  • Shuang Wang
    • 1
  • Licheng Jiao
    • 1
  1. 1.Institute of Intelligent Information Processing and National Key Lab for Radar Signal ProcessingXidian UniversityXi’anChina

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