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Gaussian MRF Rotation-Invariant Features for Image Classification
July 2004 (vol. 26 no. 7)
pp. 951-955

Abstract—Features based on Markov random field (MRF) models are sensitive to texture rotation. This paper develops an anisotropic circular Gaussian MRF (ACGMRF) model for retrieving rotation-invariant texture features. To overcome the singularity problem of the least squares estimate method, an approximate least squares estimate method is designed and implemented. Rotation-invariant features are obtained from the ACGMRF model parameters using the discrete Fourier transform. The ACGMRF model is demonstrated to be a statistical improvement over three published methods. The three methods include a Laplacian pyramid, an isotropic circular GMRF (ICGMRF), and gray level cooccurrence probability features.

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Index Terms:
Markov random field (MRF), Gaussian MRF (GMRF) model, isotropic, anisotropic, least squares estimate (LSE), discrete Fourier transform (DFT), rotational invariance, texture analysis, classification.
Citation:
Huawu Deng, David A. Clausi, "Gaussian MRF Rotation-Invariant Features for Image Classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 7, pp. 951-955, July 2004, doi:10.1109/TPAMI.2004.30
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