Rotation Invariant Texture Descriptor Using Local Shearlet-Based Energy Histograms

@article{He2013RotationIT,
  title={Rotation Invariant Texture Descriptor Using Local Shearlet-Based Energy Histograms},
  author={Jiangping He and Hongwei Ji and Xin Yang},
  journal={IEEE Signal Processing Letters},
  year={2013},
  volume={20},
  pages={905-908}
}
This letter presents a rotation invariant descriptor based on the shearlet transform for texture classification. In the presented method, texture images are first decomposed by the shearlet transform, followed by construction of local energy features. Afterwards, the local energy features are quantized and encoded to be rotation invariant. The energy histograms accumulated over all decomposition levels reflect the different energy distributions and form a new image characteristic. Our method… CONTINUE READING
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