Texture Classification Using Shift-Invariant Wavelet Packet Decomposition

@inproceedings{ChiMan2001TextureCU,
  title={Texture Classification Using Shift-Invariant Wavelet Packet Decomposition},
  author={Pun Chi-Man and Lee Moon-Chuen},
  year={2001}
}
  • Pun Chi-Man, Lee Moon-Chuen
  • Published 2001
This paper proposes a high performance texture classification method using dominant energy features based on shift-invariant wavelet packet coefficients obtained by 2D shift-invariant wavelet packet decomposition. Experiments employing a reduced feature set show that the proposed method involves a relatively small classification time while still achieving a high accuracy rate (95.6%) for classifying twenty classes of natural texture images. Key-Words: Wavelet Packets, Shift-Invariance, and… CONTINUE READING
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Texture analysis. Handbook of Pattern Recognition and Computer Vision”, chapter 2.1, pages 235--276

  • M. Tuceryan, A. K. Jain
  • World Scientific,
  • 1993
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