Multiresolution image classification by hierarchical modeling with two-dimensional hidden Markov models

IEEE Transactions on Information Theory

Volumn 46, Issue 5, 2000, Pages 1826-1841

  Li, Jia ^a   Gray, Robert M ^b   Olshen, Richard A ^c  

^a PALO ALTO RESEARCH CENTER   (United States)

^b Stanford University   (United States)

^c STANFORD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; FEATURE EXTRACTION; IMAGE SEGMENTATION; MARKOV PROCESSES; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; OPTIMIZATION; PROBABILITY; TWO DIMENSIONAL;

FEATURE VECTORS; IMAGE CLASSIFICATION; MULTISCALE INFORMATION; SUBOPTIMAL ALGORITHMS;

IMAGE ANALYSIS;

EID: 0034245909     PISSN: 00189448     EISSN: None     Source Type: Journal    
DOI: 10.1109/18.857794     Document Type: Article

References (38)

1. K. Abend, T. J. Harley, and L. N. Kanal, "Classification of binary random patterns," IEEE Trans. Inform. Theory, vol. IT-11, pp. 538-544, Oct. 1965.
2. A. Barron, J. Rissanen, and B. Yu, "The minimum description length principle in coding and modeling," IEEE Trans. Inform. Theory, vol. 44, pp. 2743-2760, Oct. 1998.
3. M. Basseville, A. Benveniste, K. C. Chou, S. A. Golden, R. Nikoukhah, and A. S. Willsky, "Modeling and estimation of multiresolution stochastic processes," IEEE Trans. Inform. Theory, vol. 38, pp. 766-784, Mar. 1992. 4 P. J. Bickel and K. A. Doksum, Mathematical Statistics: Basic Ideas and Selected Topics. Englewood Cliffs, NJ: Prentice-Hall, 1977.
4. T. Binford, T. Levitt, and W. Mann, "Bayesian inference in model-based machine vision," Uncertainty in Artificial Intelligence, 1988.
5. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees. London, U.K.: Chapman & Hall, 1984.
6. C. A. Bouman and M. Shapiro, "A multiscale random field model for Bayesian image segmentation," IEEE Trans. Image Processing, vol. 3, pp. 162-177, Mar. 1994.
7. J. M. Boyett, "Random RxC tables with given row and column totals," Appl. Statist., vol. 28, pp. 329-332, 1979.
8. H. Choi and R. G. Baraniuk, "Image segmentation using wavelet-domain classification," in Proc. SPIE Tech. Conf. Mathematical Modeling, Bayesian Estimation, and Inverse Problems, July 1999, pp. 306-320.
9. G. F. Cooper, "The computational complexity of probabilistic inference using Bayesian belief networks," Artif. Intel!., vol. 42, pp. 393105, 1990.
10. M. S. Grouse, R. D. Nowak, and R. G. Baraniuk, "Wavelet-based statistical signal processing using hidden Markov models," IEEE Trans. Signal Processing, vol. 46, pp. 886-902, Apr. 1998.
11. I. Daubechies, Ten Lectures on Wavelets. Philadelphia, PA: SIAM, 1992.
12. A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. Roy. Statist. Soc., vol. 39, no. 1, pp. 1-21, 1977.
13. P. A. Devijver, "Probabilistic labeling in a hidden second order Markov mesh," in Pattern Recognition in Practice II. Amsterdam, The Netherlands: Elsevier, 1985, pp. 113-23.
14. R. A. Fisher, The Design of Experiments. Edinburgh, Scotland: Oliver and Boyd, 1953.
15. C. H. Fosgate, H. Krim, W. W. Irving, W. C. Karl, and A. S. Willsky, "Multiscale segmentation and anomaly enhancement of SAR imagery," IEEE Trans. Image Processing, vol. 6, pp. 7-20, Jan. 1997.
16. W. T. Freeman and E. C. Pasztor, "Learning low-level vision," in Proc. 7th Int. Conf. Computer Vision, Corfu, Greece, Sept. 1999.
17. R. G. Gallager, Information Theory and Reliable Communication. New York: Wiley, 1968.
18. D. Heckerman, "A tutorial on learning with Bayesian Networks," Microsoft Res., Tech. Rep. MSR-TR-95-06, Nov. 1996.
19. D. Knill and W. Richards, Eds., Perception as Bayesian Inference. Cambridge, U.K.: Cambridge Univ. Press, 1996.
20. T. Kohonen, Self-Organization and Associative Memory. Berlin, Germany: Springer-Verlag, 1989.
21. M. S. Landy and J. A. Movshon, Eds., Computational Models of Visual Processing. Cambridge, MA: MIT Press, 1991.
22. L. C. Lazzeroni and K. Lange, "Markov chains for Monte Carlo tests of genetic equilibrium in multidimensional contingency tables," Ann. Statist., vol. 25, pp. 138-168, 1997.
23. E. Levin and R. Pieraccini, "Dynamic planar warping for optical character recognition," in Int. Conf. Acoustics, Speech, and Signal Processing, vol. 3, San Francisco, CA, Mar. 1992, pp. 149-152.
24. J. Li, A. Najmi, and R. M. Gray, "Image classification by a two dimensional hidden Markov model," IEEE Trans. Signal Processing, vol. 48, pp. 517-33, Feb. 2000.
25. G. Loum, P. Provent, J. Lemoine, and E. Petit, "A new method for texture classification based on wavelet transform," in Proc. 3rd Int. Symp. TimeFrequency and Time-Scale Analysis, June 1996, pp. 29-32.
26. C. R. Mehta and N. R. Patel, "A network algorithm for performing Fisher's exact test in r X c contingency tables," J. Amer. Statist. Assoc., vol. 78, pp. 42734, June 1983.
27. Y. Meyer, Wavelets Algorithms and Applications. Philadelphia, PA: SIAM, 1993.
28. R. D. Nowak, "Multiscale hidden Markov models for Bayesian image analysis," in Bayesian Inference in Wavelet Based Models. New York: Springer-Verlag, 1999, pp. 243-266.
29. K. L. Oehler, "Image compression and classification using vector quantization," Ph.D. dissertation, Stanford Univ., Stanford, CA, 1993.
30. K. L. Oehler and R. M. Gray, "Combining image compression and classification using vector quantization," IEEE Trans. Pattern Anal. Machine Intell, vol. 17, pp. 461-73, May 1995.
31. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Francisco, CA: Morgan Kaufmann, 1988.
32. P. Ferez and F. Heitz, "Restriction of a Markov random field on a graph and multiresolution statistical image modeling," IEEE Trans. Inform. Theory, vol. 42, pp. 180-90, Jan. 1996.
33. K. O. Perlmutter, S. M. Perlmutter, R. M. Gray, R. A. Olshen, and K. L. Oehler, "Bayes risk weighted vector quantization with posterior estimation for image compression and classification," IEEE Trans. Image Processing, vol. 5, pp. 347-60, Feb. 1996.
34. C. E. Shannon, "A mathematical theory of communication," Bell Syst. Tech. ., vol. 27, pp. 37923, July 1948.
35. M. Stone, "Cross-validation: A review," Math. Operationforsch. Statist. Ser. Statist., pp. 127-39, 1978.
36. M. Unser, "Texture classification and segmentation using wavelet frames," IEEE Trans. Image Processing, vol. 4, pp. 1549-60, Nov. 1995.
37. A. J. Viterbi and J. K. Omura, "Trellis encoding of memoryless discrete-time sources with a fidelity criterion," IEEE Trans. Inform. Theory, vol. IT-20, pp. 325-332, May 1974.
38. S. Young, J. Jansen, J. Odell, D. Ollason, and P. Woodland, HTK-Hidden Markov Model Toolkit. Cambridge, U.K.: Cambridge Univ. Press, 1995.