Image segmentation using hidden Markov Gauss mixture models

IEEE Transactions on Image Processing

Volumn 16, Issue 7, 2007, Pages 1902-1911

  Pyun, Kyungsuk ^a,b   Lim, Johan ^c   Won, Chee Sun ^d   Gray, Robert M ^a  

^a Stanford University   (United States)

^b HEWLETT PACKARD COMPANY   (United States)

^c Yonsei University   (South Korea)

^d Dongguk University   (South Korea)

Author keywords

2 D hidden Markov models (HMMs); Bond percolation (BP) model; Gauss mixture models (GMMs); Gauss mixture vector quantizer (GMVQ); Image classification; Image segmentation; Parameter estimation

Indexed keywords

HIDDEN MARKOV MODELS; IMAGE CLASSIFICATION; OPTIMIZATION; PARAMETER ESTIMATION; PROBABILITY DISTRIBUTIONS; TWO DIMENSIONAL; VECTOR QUANTIZATION;

BOND-PERCOLATION MODEL; GAUSS MIXTURE MODEL; MINIMUM DISCRIMINATION INFORMATION;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; IMAGE ENHANCEMENT; METHODOLOGY; NORMAL DISTRIBUTION; PROBABILITY; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MARKOV CHAINS; MODELS, STATISTICAL; NORMAL DISTRIBUTION; PATTERN RECOGNITION, AUTOMATED;

EID: 34347380736     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2007.899612     Document Type: Article

References (44)

1. A. Aiyer, K. Pyun, Y. Huang, D. B. O'Brien, and R. M. Gray, "Lloyd clustering of Gauss mixture models for image compression and classification," Signal Process.: Image Commun., vol. 20, pp. 459-485, 2005.
2. A. K. Aiyer, "Robust image compression using gauss mixture models," Ph.D. dissertation, Elect. Eng. Dept., Stanford Univ., Stanford, CA, Aug. 2001.
3. D. Benboudjema and W. Pieczynski, "Unsupervised image segmentation using triplet Markov fields," Comput. Vis. Image Understand., vol. 99, no. 3, pp. 476-498, 2005.
4. J. Besag, "On the statistical analysis of dirty pictures," J. Roy. Statist. Soc. B, vol. 48, pp. 259-302, 1986.
5. J. Besag and P. J. Green, "Spatial statistics and Baysian computation," J. Roy. Statist. Soc. B., vol. 55, pp. 25-37, 1993.
6. J. Blanchet, F. Forbes, and C. Schmid, "Triplet Markov field designed for supervised classification of texture images," presented at the COMPSTAT, Rome, Italy, Aug. 2006.
7. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees. Belmont, CA: Chapman & Hall, 1984.
8. P. Brodatz, Textures: Photographic Album for Artists & Designers. New York: Dover, 1966.
9. G. Celeux and J. Diebolt, "The SEM algorithm: A probabilisitic teacher algorithm derived from the EM algorithm for the mixture," Comput. Statist. Quart., vol. 2, pp. 73-82, 1985.
10. G. Celeux, G. Forbes, and N. Peyrard, "EM procedures using mean field-like approximations for Markov model-based image segmentation," Pattern Recognit., vol. 36, no. 1, pp. 131-144, 2003.
11. M. L. Comer and E.-J. Delp, "Segmentation of textured images using a multiresolution Gaussian autoregressive model," IEEE Trans. Image Process., vol. 8, no. 3, pp. 408-420, Mar. 1999.
12. T. M. Cover and J.A. Thomas Elements of Information Theory New York: Wiley, 1991.
13. 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.
14. S. Geman and D. Geman, "Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images," IEEE Trans. Pattern Anal. Mach. Intell., vol. 11, no. 6, pp. 689-691, Jun. 1984.
15. A. Gersho and R. M. Gray, Vector Quantization and Signal Compression Boston, MA: Kluwer, 1992.
16. R. M. Gray, "Gauss mixture vector quantization," in Proc. Int. Conf. Acoust., Speech, Signal Processing, 2001, pp. 1769-1772.
17. R. M. Gray, A. H. Gray, Jr., G. Rebolledo, and J. E. Shore, "Rate distortion speech coding with a minimum discrimination information distortion measure," IEEE Trans. Inf. Theory, vol. 27, no. 6, pp. 708-721, Nov. 1981.
18. R. M. Gray and T. Linder, "Mismatch in high rate entropy constrained vector quantization," IEEE Trans. Inf. Theory, vol. 49, no. 5, pp. 1204-1217, May 2003.
19. G. R. Grimmett, Percolation. New York: Springer, 1999.
20. T. Hastie and R. Tibshirani, "Discriminant analysis by Gaussian mixtures," J. Roy. Statist. Soc. B, vol. 58, pp. 155-176, 1996.
21. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning. Berlin, Germany: Springer-Verlag, 2001.
22. T. Kohonen, Self-Organization and Associative Memory. Berlin, Germany: Springer-Verlag, 1989.
23. A. Lapidoth, "On the role of mismatch in rate distortion theory," IEEE Trans. Inf. Theory, vol. 43, no. 1, pp. 38-47, Jan. 1997.
24. M. Lavielle and E. Moulines, "A simulated annealing version of the EM algorithm for non-Gaussian deconvolution," Statist. Comput., vol. 7, no. 4, pp. 229-236, 1997.
25. J. Li and R. M. Gray, "Image classification based on a multi-resolution two dimensional hidden Markov model," in Proc. Int. Image Processing 1999, pp. 348-352.
26. J. Li, A. Najmi, and R. M. Gray, "Image classification by a two-dimensional hidden Markov model," IEEE Trans. Signal Process. vol. 48, no. 2, pp. 517-533, Feb. 2000.
27. J. S. Liu, Monte Carlo Strategies in Scientific Computing. Berlin, Germany: Springer-Verlag, 2001.
28. J. Mao and A. K. Jain, "Texture classification and segmentation using multiresolution simultaneous autoregressive models," IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 2, pp. 173-188, Feb. 1992.
29. G. J. McLachlan and K. E. Basford, Mixture Models: Inference and Applications to Clustering. New York: Marcel Dekker, 1988.
30. D. Melas and S. P. Wilson, "Double Markov random fields and bayesian image segmentation," IEEE Trans. Signal Process., vol. 50, no. 2, pp. 357-365, Feb. 2002.
31. K. L. Oehler and R. M. Gray, J. A. Storer and M. Cohn, Eds., "Combining image classification and image compression using vector quantization," in Proc. IEEE Data Compression Conf., Snowbird, UT, Mar. 1993, pp. 2-11.
32. K. O. Perlmutter, "Compression and classification of images using vector quantization and decision trees," Ph.D. dissertation, Elect. Eng. Dept., Stanford Univ., Stanford, CA, 1995.
33. W. Pieczynski and A.-N. Tebbache, "Pairwise Markov random fields and segmentation of textured images," Mach. Graph. Vis., vol. 9, no. 3, pp. 705-718, 2000.
34. K. Pyun, C. S. Won, J. Lim, and R. M. Gray, "Robust image classification based on a non-causal hidden Markov Gauss mixture model," presented at the Int. Conf. Image Processing, Rochester, NY, Sep. 2002.
35. K. Pyun, C. S. Won, J. Lim, and R. M. Gray, "Texture classification based on multiple Gauss mixture vector quantizers," in Proc. Int. Conf. Multimedia and Expo, Aug. 2002, vol. 2, pp. 501-504.
36. S. Reed, Y. Petillot, and J. Bell, "An automatic approach to the detection and extraction of mine features in sidescan sonar," IEEE J. Ocean. Eng., vol. 28, no. 1, pp. 90-105, Jan. 2003.
37. D. J. Sakrison, "Worst sources and robust codes for difference distortion measures," IEEE Trans. Inf. Theory, vol. 21, no. 5, pp. 301-309, May 1975.
38. N. Ueda and R. Nakano, "Deterministic annealing variant of the EM algorithm," in Proc. Neural Information Processing Systems 7, 1995, pp. 545-552.
39. G. C. G. Wei and M. A. Tanner, "Applications of multiple imputation to the analysis of censored regression data," Biometrics, vol. 47, no. 4, pp. 1297-1309, Dec. 1991.
40. G. Winkler, Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction. Berlin, Germany: Springer, 2003.
41. W. K. Hastings, "Monte Carlo sampling methods using Markov chains and their applications," Biometrika, vol. 57, pp. 97-109, 1970.
42. C. S. Won and R. M. Gray, Stochastic Image Processing. Norwell, MA: Kluwer, 2004.
43. C. S. Won, K. Pyun, and R. M. Gray, "Automatic object segmentation in images with low depth of field," presented at the Int. Conf. Image Processing, Rochester, NY, Sep. 2002.
44. Z. Zhou, R. N. Leahy, and J. Qi, "Approximate maximum likelihood hyperparameter estimation for Gibbs priors," IEEE Trans. Image Process., vol. 6, no. 6, pp. 844-861, Jun. 1997.