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Gaussian MRF rotation-invariant features for image classification

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2 Author(s)
Huawu Deng ; Dept. of Syst. Design Eng., Waterloo Univ., Ont., Canada ; D. A. Clausi

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.

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:26 ,  Issue: 7 )