Issue No. 02 - February (1991 vol. 13)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.67648
<p>Consideration is given to the problem of classifying a test textured image that is obtained from one of C possible parent texture classes, after possibly applying unknown rotation and scale changes to the parent texture. The training texture images (parent classes) are modeled by Gaussian Markov random fields (GMRFs). To classify a rotated and scaled test texture, the rotation and scale changes are incorporated in the texture model through an appropriate transformation of the power spectral density of the GMRF. For the rotated and scaled image, a bona fide likelihood function that shows the explicit dependence of the likelihood function on the GMRF parameters, as well as on the rotation and scale parameters, is derived. Although, in general, the scaled and/or rotated texture does not correspond to a finite-order GMRF, it is possible nonetheless to write down a likelihood function for the image data. The likelihood function of the discrete Fourier transform of the image data corresponds to that of a white nonstationary Gaussian random field, with the variance at each pixel (i,j) being a known function of the rotation, the scale, the GMRF model parameters, and (i,j). The variance is an explicit function of the appropriately sampled power spectral density of the GMRF. The estimation of the rotation and scale parameters is performed in the frequency domain by maximizing the likelihood function associated with the discrete Fourier transform of the image data. Cramer-Rao error bounds on the scale and rotation estimates are easily computed. A modified Bayes decision rule is used to classify a given test image into one of C possible texture classes. The classification power of the method is demonstrated through experimental results on natural textures from the Brodatz album.</p>
pattern recognition; parameter estimation; rotated textured images; scaled textured images; Gaussian Markov random field models; parent texture classes; training texture images; likelihood function; discrete Fourier transform; white nonstationary Gaussian random field; power spectral density; Cramer-Rao error bounds; modified Bayes decision rule; classification power; Brodatz album; Bayes methods; decision theory; parameter estimation; pattern recognition; random processes
F. Cohen, M. Patel and Z. Fan, "Classification of Rotated and Scaled Textured Images Using Gaussian Markov Random Field Models," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 13, no. , pp. 192-202, 1991.