Likelihood calculation for a class of multiscale stochastic models, with application to texture discrimination

A class of multiscale stochastic models based on scale-recursive dynamics on trees has previously been introduced. Theoretical and experimental results have shown that these models provide an extremely rich framework for representing both processes which are intrinsically multiscale, e.g., 1/f processes, as well as 1D Markov processes and 2D Markov random fields. Moreover, efficient optimal estimation algorithms have been developed for these models by exploiting their scale-recursive structure. The authors exploit this structure in order to develop a computationally efficient and parallelizable algorithm for likelihood calculation. They illustrate one possible application to texture discrimination and demonstrate that likelihood-based methods using the algorithm achieve performance comparable to that of Gaussian Markov random field based techniques, which in general are prohibitively complex computationally.

[1]  W. Clem Karl,et al.  Multiscale representations of Markov random fields , 1993, IEEE Trans. Signal Process..

[2]  Mark R. Luettgen,et al.  Image processing with multiscale stochastic models , 1993 .

[3]  Patrick A. Kelly,et al.  Adaptive segmentation of speckled images using a hierarchical random field model , 1988, IEEE Trans. Acoust. Speech Signal Process..

[4]  W. C. Karl,et al.  Eecient Multiscale Regularization with Applications to the Computation of Optical Flow 1 , 1993 .

[5]  Alan S. Willsky,et al.  Multiscale smoothing error models , 1995, IEEE Trans. Autom. Control..

[6]  R. Chellappa,et al.  Digital image restoration using spatial interaction models , 1982 .

[7]  F. S. Cohen,et al.  Classification of Rotated and Scaled Textured Images Using Gaussian Markov Random Field Models , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  R. D. Chaney,et al.  Optimal Processing of Polarimetric Synthetic-Aperture Radar Imagery , 1990 .

[9]  Charles A. Bouman,et al.  Multispectral image segmentation using a multiscale model , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[10]  Stéphane Mallat,et al.  Multifrequency channel decompositions of images and wavelet models , 1989, IEEE Trans. Acoust. Speech Signal Process..

[11]  W. Clem Karl,et al.  Efficient multiscale regularization with applications to the computation of optical flow , 1994, IEEE Trans. Image Process..

[12]  K. C. Chou,et al.  Recursive and iterative estimation algorithms for multiresolution stochastic processes , 1989, Proceedings of the 28th IEEE Conference on Decision and Control,.

[13]  Rama Chellappa,et al.  Texture classification using features derived from random field models , 1982, Pattern Recognit. Lett..

[14]  Rama Chellappa,et al.  Unsupervised Texture Segmentation Using Markov Random Field Models , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  David C. Munson,et al.  A signal processing view of strip-mapping synthetic aperture radar , 1989, IEEE Trans. Acoust. Speech Signal Process..

[16]  Leslie M. Novak,et al.  Optimal Speckle Reduction In Polarimetric Sar Imagery* , 1988, Twenty-Second Asilomar Conference on Signals, Systems and Computers.

[17]  Rama Chellappa,et al.  Texture segmentation with neural networks , 1992 .

[18]  I. Daubechies.  Orthonormal bases of compactly supported wavelets , 1988 .

[19]  H. Derin,et al.  Modeling and segmentation of speckled images using complex data , 1990 .

[20]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems , 1988 .

[21]  Rama Chellappa,et al.  Classification of textures using Gaussian Markov random fields , 1985, IEEE Trans. Acoust. Speech Signal Process..

[22]  K. C. Chou,et al.  Multiscale recursive estimation, data fusion, and regularization , 1994, IEEE Trans. Autom. Control..