Gaussian MRF rotation-invariant features for image classification.
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.
Deng
Huawu
H
Department of Systems Design, Engineering, University of Waterloo, Ontario, Canada. h2deng@engmail.uwaterloo.ca
Clausi
David A
DA
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008390:Markov Chains; D015233:Models, Statistical; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D012399:Rotation