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Rotation and Gray Scale Transform Invariant Texture Identification using Wavelet Decomposition and Hidden Markov Model
February 1994 (vol. 16 no. 2)
pp. 208-214

In this correspondence, we have presented a rotation and gray scale transform invariant texture recognition scheme using the combination of quadrature mirror filter (QMF) bank and hidden Markov model (HMM). In the first stage, the QMF bank is used as the wavelet transform to decompose the texture image into subbands. The gray scale transform invariant features derived from the statistics based on first-order distribution of gray levels are then extracted from each subband image. In the second stage, the sequence of subbands is modeled as a hidden Markov model (HMM), and one HMM is designed for each class of textures. The HMM is used to exploit the dependence among these subbands, and is able to capture the trend of changes caused by rotation. During recognition, the unknown texture is matched against all the models. The best matched model identifies the texture class. Up to 93.33% classification accuracy is reported.

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Index Terms:
image texture; wavelet transforms; hidden Markov models; filtering and prediction theory; digital filters; rotation-invariant texture identification; gray scale transform invariant texture identification; wavelet decomposition; hidden Markov model; texture recognition scheme; quadrature mirror filter bank; HMM; QMF bank; wavelet transform; texture image decomposition; image subbands
Citation:
J.L. Chen, A. Kundu, "Rotation and Gray Scale Transform Invariant Texture Identification using Wavelet Decomposition and Hidden Markov Model," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 2, pp. 208-214, Feb. 1994, doi:10.1109/34.273730
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