An extension of the standard mixture model for image segmentation

IEEE Transactions on Neural Networks

Volumn 21, Issue 8, 2010, Pages 1326-1338

  Nguyen, Nguyen ^a   Wu, Q M Jonathan ^a   Ahuja, Siddhant ^a  

^a UNIVERSITY OF WINDSOR   (Canada)

Author keywords

Gradient method; image segmentation; spatial constraints; standard Gaussian mixture model

Indexed keywords

GAUSSIAN MIXTURE MODEL; GAUSSIAN MIXTURE MODELING; GRAY-SCALE IMAGES; IMAGE PIXELS; LOG LIKELIHOOD; MARKOV RANDOM FIELD MODELS; MIXTURE MODEL; MODEL PARAMETERS; SEGMENTATION RESULTS; SPATIAL CONSTRAINTS; SPATIAL DEPENDENCE; SPATIAL RELATIONSHIPS;

DIGITAL IMAGE STORAGE; GAUSSIAN DISTRIBUTION; GRADIENT METHODS; PIXELS; STANDARDS;

IMAGE SEGMENTATION;

ALGORITHM; ANIMAL; ARTICLE; ARTIFACT; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; COMPUTER SIMULATION; CONTRAST SENSITIVITY; DEPTH PERCEPTION; HUMAN; IMAGE PROCESSING; METHODOLOGY; NORMAL DISTRIBUTION; PHYSIOLOGY; STANDARD;

ALGORITHMS; ANIMALS; ARTIFACTS; COMPUTER SIMULATION; CONTRAST SENSITIVITY; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; NEURAL NETWORKS (COMPUTER); NORMAL DISTRIBUTION; PATTERN RECOGNITION, AUTOMATED; SPACE PERCEPTION;

EID: 77955515108     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2010.2054109     Document Type: Article

References (43)

1. J. Besag, "On the statistical analysis of dirty pictures," J. Roy. Statist. Soc. Ser. B, vol.48, no.3, pp. 259-302, 1986.
2. C. M. Bishop, Neural Networks for Pattern Recognition. Oxford, U.K.: Oxford University Press, 1995, pp. 59-73, 257-292.
3. C. M. Bishop, Pattern Recognition and Machine Learning. Berlin, Germany: Springer, 2006.
4. K. Blekas, A. Likas, N. P. Galatsanos, and I. E. Lagaris, "A spatially constrained mixture model for image segmentation," IEEE Trans. Neural Netw., vol.16, no.2, pp. 494-498, Mar. 2005.
5. W. Cai, S. Chen, and D. Zhang, "Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation," Pattern Recognit., vol.40, no.3, pp. 825-838, 2007.
6. C. Carson, S. Belongie, H. Greenspan, and J. Malik, "Blobworld: Image segmentation using expectation-maximization and its application to image querying," IEEE Trans. Pattern Anal. Mach. Intell., vol.24, no.8, pp. 1026-1038, Aug. 2002.
7. G. Celeux, F. 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.
8. S. P. Chatzis and T. A. Varvarigou, "A fuzzy clustering approach toward hidden Markov random field models for enhanced spatially constrained image segmentation," IEEE Trans. Fuzzy Syst., vol.16, no.5, pp. 1351- 1361, Oct. 2008.
9. H. S. Choi, D. R. Haynor, and Y. Kim, "Partial volume tissue classification of multichannel magnetic resonance images: A mixel model," IEEE Trans. Med. Imaging, vol.10, no.3, pp. 395-407, Sep. 1991.
10. M. Dang and G. Govaert, "Spatial fuzzy clustering using EM and Markov random fields," Int. J. Syst. Res. Inform. Sci., vol.8, no.4, pp. 183-202, 1998.
11. A. Diplaros, N. Vlassis, and T. Gevers, "A spatially constrained generative model and an EM algorithm for image segmentation," IEEE Trans. Neural Netw., vol.18, no.3, pp. 798-808, May 2007.
12. M. A. T. Figueiredo and A. K. Jain, "Unsupervised learning of finite mixture models," IEEE Trans. Pattern Anal. Mach. Intell., vol.24, no.3, pp. 381-396, Mar. 2002.
13. R. Fjortoft, Y. Delignon, W. Pieczynski, M. Sigelle, and F. Tupin, "Unsupervised classification of radar images using hidden Markov chains and hidden Markov random fields," IEEE Trans. Geosci. Remote Sens., vol.41, no.3, pp. 675-686, Mar. 2003.
14. F. Forbes and N. Peyrard, "Hidden Markov random field model selection criteria based on mean field-like approximations," IEEE Trans. Pattern Anal. Mach. Intell., vol.25, no.9, pp. 1089-1101, Sep. 2003.
15. S. Geman and D. Geman, "Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images," IEEE Trans. Pattern Anal. Mach. Intell., vol.PAMI-6, no.6, pp. 721-741, Nov. 1984.
16. P. Haim, F. Joseph, and J. Ian, "A study of Gaussian mixture models of color and texture features for image classification and segmentation," Pattern Recognit., vol.39, no.4, pp. 695-706, 2006.
17. A. K. Jain, R. P. W. Duin, and J. C. Mao, "Statistical pattern recognition: A review," IEEE Trans. Pattern Anal. Mach. Intell., vol.22, no.1, pp. 4-37, Jan. 2000.
18. S. Kumar and M. Hebert, "Discriminative random fields," Int. J. Comput. Vision, vol.68, no.2, pp. 179-201, 2006.
19. S. Kumar and M. Hebert, "Discriminative random fields: A discriminative framework for contexual interaction in classification," in Proc. IEEE Conf. Comput. Vision, Oct. 2003, pp. 1150-1157.
20. J. M. Laferte, P. Perez, and F. Heitz, "Discrete Markov image modeling and inference on the quadtree," IEEE Trans. Image Process., vol.9, no.3, pp. 390-404, May 2000.
21. J. Lafferty, A. McCallum, and F. Pereira, "Conditional random fields: Probabilistic models for segmenting and labeling sequence data," in Proc. Int. Conf. Mach. Learning, Jun. 2001, pp. 282-289.
22. C. H. Lee, R. Greiner, and M. Schmidt, "Support vector random fields for spatial classification," in Proc. PKDD, 2005, pp. 121-132.
23. S. Z. Li, Markov Random Field Modeling in Image Analysis. Berlin, Germany: Springer-Verlag, 2009, pp. 43-45.
24. J. L. Marroquin, E. A. Santana, and S. Botello, "Hidden Markov measure field models for image segmentation," IEEE Trans. Pattern Anal. Mach. Intell., vol.25, no.11, pp. 1380-1387, Nov. 2003.
25. D. Martin, C. Fowlkes, D. Tal, and J. Malik, "A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics," in Proc. Int. Conf. Comput. Vision, Jul. 2001, pp. 416-423.
26. G. J. McLachlan and D. Peel, Finite Mixture Models. New York: Wiley, 2000.
27. G. J. McLachlan and T. Krishnan, The EM Algorithm and Extensions (Wiley Series in Probability and Statistics). New York: Wiley, 1997.
28. N. Nasios and A. G. Bors, "Variational learning for Gaussian mixture models," IEEE Trans. Syst. Man Cybern. B Cybern., vol.36, no.4, pp. 849-862, Aug. 2006.
29. J. N. Provost, R. C. Collet, P. Perez, and P. Bouthemy, "Hierarchical Markovian segmentation of multispectral images for the reconstruction of water depth maps," Comput. Vision Image Understanding, vol.93, no.2, pp. 155-174, 2004.
30. W. Qian and D. M. Titterington, "Estimation of parameters in hidden Markov models," Philos. Trans. Roy. Soc. London A Math. Phys. Sci., vol.337, no.1647, pp. 407-428, 1991.
31. R. A. Redner and H. F. Walker, "Mixture densities, maximum likelihood and the EM algorithm," SIAM Rev., vol.26, no.2, pp. 195-239, 1984.
32. M. Robinson, M. Azimi-Sadjadi, and J. Salazar, "A temporally adaptive classifier for multispectral imagery," IEEE Trans. Neural Netw., vol.15, no.1, pp. 159-165, Jan. 2004.
33. Y. D. Rubinstein and T. Hastie, "Discriminative vs. informative learning," in Proc. 3rd Int. Conf. Knowl. Discovery Data Mining, Aug. 1997, pp. 49-53.
34. W. Rudin, Real and Complex Analysis, 3rd ed. New York: McGraw-Hill, 1987, pp. 62-65.
35. G. S. Sanjay and T. J. Hebert, "Bayesian pixel classification using spatially variant finite mixtures and the generalized EM algorithm," IEEE Trans. Image Process., vol.7, no.7, pp. 1014-1028, Jul. 1998.
36. P. Santago and H. D. Gage, "Quantification of MR brain images by mixture density and partial volume modeling," IEEE Trans. Med. Imaging, vol.12, no.3, pp. 566-574, Sep. 1993.
37. P. Santago and H. D. Gage, "Statistical models of partial volume effect," IEEE Trans. Image Process., vol.4, no.11, pp. 1531-1540, Nov. 1995.
38. G. A. F. Seber, Multivariate Observations. New York: Wiley, 1984, pp. 482-488.
39. M. N. Thanh and Q. M. J. Wu, "Maximum likelihood neural network based on the correlation among neighboring pixels for noisy image segmentation," in Proc. IEEE Int. Conf. Image Process., Oct. 2008, pp. 3020-3023.
40. D. M. Titterington, A. F. M. Smith, and U. E. Makov, Statistical Analysis of Finite Mixture Distributions. Hoboken, NJ: Wiley, 1985.
41. R. Unnikrishnan, C. Pantofaru, and M. Hebert, "A measure for objective evaluation of image segmentation algorithms," in Proc. IEEE Conf. Comput. Vision Pattern Recognit., Jun. 2005, pp. 34-41.
42. J. Zhang, "The mean field theory in EM procedures for Markov random fields," IEEE Trans. Signal Process., vol.40, no.10, pp. 2570-2583, Oct. 1992.
43. Y. Zhang, M. Brady, and S. Smith, "Segmentation of brain MR images through a hidden Markov random field model and the expectationmaximization algorithm," IEEE Trans. Med. Imaging, vol.20, no.1, pp. 45-57, Jan. 2001.