Mixture model and Markov random field-based remote sensing image unsupervised clustering method

Opto-Electronics Review

Volumn 19, Issue 1, 2011, Pages 83-88

  Hou, Y ^a   Yang, Y ^b,c   Rao, N ^c   Lun, X ^a   Lan, J ^a  

^a NORTHEAST ELECTRIC POWER UNIVERSITY   (China)

^b JIANGXI UNIVERSITY OF FINANCE AND ECONOMICS   (China)

^c UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

iterative conditional model; Markov random field; maximum a posteriori; remote sensing image

Indexed keywords

CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; GAUSSIAN DISTRIBUTION; IMAGE SEGMENTATION; ITERATIVE METHODS; MARKOV PROCESSES; SIMULATED ANNEALING; MAGNETORHEOLOGICAL FLUIDS;

CONDITIONAL MODELS; GAUSSIAN MIXTURE MODEL; INTENSITY DIFFERENCE; MARKOV RANDOM FIELDS; MAXIMUM A POSTERIORI; MAXIMUM A POSTERIORS; REMOTE SENSING IMAGES; UNSUPERVISED CLUSTERING METHODS;

REMOTE SENSING;

EID: 78650753717     PISSN: 12303402     EISSN: 18963757     Source Type: Journal    
DOI: 10.2478/s11772-010-0070-3     Document Type: Article

References (23)

1. thInt. Conf. on Knowledge-Based & Intelligent Information & Engineering Systems, Bournemouth, 17-24 (2006).
2. L. Wang, W. Wu, Q. Dai, and Q. Qin, "Remote sensing image texture classification based on Gabor wavelet and support vector machine", Geoinformatics (China) 6419, 413-419 (2006).
3. C. R. Jung, "Unsupervised multiscale segmentation of colour images", Pattern Recogn. Lett. 28, 523-533 (2007).
4. U. C. Benz, P. Hofman, G. Willhauck, I. Lingenfelder, and M. Heynen, "Multi-resolusion object-oriented fuzzy analysis of remote sensing data for GIS-ready information", ISPRS J. Photogramm. 58, 239-258 (2004).
5. F. Forbes and G. Fort, "Combining Monte Carlo and mean-field-like methods for inference in hidden Markov random fields", IEEE T. Image Process. 16, 824-837 (2007).
6. R. Nishii and S. Eguchi, "Supervised image classification by contextual AdaBoost based on posteriors in neighbourhoods", IEEE T. Geosci. Remote43, 2547-2554 (2005).
7. F. Destrempes, M. Mignotte, and J. F. Angers, "A stochastic method for Bayesian estimation of hidden Markov random field models with application to a colour model", IEEE T. Image Process. 14, 1096-1108 (2005).
8. J. Wu and C. S. Albert, "A segmentation model using compound Markov random fields based on a boundary model", IEEE T. Image Process. 16, 241-252 (2007).
9. X. Liu, D. L. Langer, M. A. Haider, Y. Yang, M. N. Wernick, and I. S. Yetik. "Prostate cancer segmentation with simultaneous estimation of Markov random field parameters and class", IEEE T. Med. Imaging28, 906-915 (2009).
10. X. F. Wang and X. P. Zhang, "A new localized superpixel Markov random field for image segmentation", IEEE Int. Conf. on Multimedia & Expo (ICME 2009), Mexico, 642-645 (2009).
11. B. Fischer and J. M. Buhmann, "Path-based clustering for grouping of smooth curves and texture segmentation", IEEE T. Pattern Anal. 25, 513-518 (2003).
12. A. Topchy, A. K. Jain, and W. Punch, "Combining multiple weak clustering", in Proc. IEEE Int. Conf. on Data Mining, Melbourne, FL, 331-338 (2003).
13. A. Strehl and J. Ghosh, "Cluster ensembles-acknowledge reuse framework for combining multiple partitions", J. Mach. Learn. Res. 3, 583-617 (2002).
14. T. Lei and J. Udupa, "Performance evaluation of finite normal mixture model-based image segmentation techniques", IEEE T. Image Process. 12, 1153-1169 (2003).
15. thInt. Conf. on Machine Learning, Morgan Kaufmann, San Francisco, CA, 609-616 (2001).
16. A. Vetro, Y. Wang, and H. Sun, "Rate-distortion modelling for multiscale binary shape coding based on Markov random fields", IEEE T. Image Process. 12, 356-364 (2003).
17. B. C. K. Tso and P. M. Mather, "Classification of multisource remote sensing imagery using a genetic algorithm and Markov random fields", IEEE T. Geosci. Remote37, 1255-1260 (1999).
18. M. A. Hurn, K. V. Mardia, T. J. Hainsworth, J. Kirkbride, and E. Berry, "Bayesian fused classification of medical images", IEEE T. Med. Imaging15, 850-858 (1996).
19. X. Jia and J. A. Richards, "Managing the spectral-spatial mix in context classification using Markov random fields", IEEE Geosci. Remote S. 5, 311-314 (2008).
20. J. M. Laferté, P. Perez, and F. Heitz, "Discrete Markov image modelling and inference on the quadtree", IEEE T. Image Process. 9, 390-404 (2000).
21. V. A. Tolpekin and N. A. S. Hamm, "Fuzzy super resolution mapping based on Markov random fields", IEEE Geosci. Remote Sensing Symp., Boston, 875-878 (2008).
22. D. W. Kim, K. H. Lee, and D. Lee, "On cluster validity index for estimation of the optimal number of fuzzy clusters", Pattern Recogn. 37, 2009-2025 (2004).
23. J. C. Bezdek and R. Hathaway, "Convergence theory for fuzzy C-Means: Counter examples and repairs [J]", IEEE T. Syst. Man Cyb. 17, 873-877 (1987).