Sparseness achievement in hidden markov models

Proceedings - 14th International conference on Image Analysis and Processing, ICIAP 2007

Volumn , Issue , 2007, Pages 67-72

  Bicego, Manuele ^a   Cristani, Marco ^b   Murino, Vittorio ^b  

^a UNIVERSITY OF SASSARI   (Italy)

^b UNIVERSITY OF VERONA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; COMPUTATIONAL GRAMMARS; CONFORMAL MAPPING; HIDDEN MARKOV MODELS; IMAGE ANALYSIS; LEARNING ALGORITHMS; LEARNING SYSTEMS; MARKOV PROCESSES; MATHEMATICAL MODELS; MODAL ANALYSIS; NEURAL NETWORKS; OBJECT RECOGNITION; PARAMETER ESTIMATION; SPEECH RECOGNITION;

2D SHAPES; BAYESIAN FORMULATIONS; BAYESIAN FRAMEWORKS; COMPARATIVE EVALUATIONS; DIRICHLET PRIORS; MAXIMUM A POSTERIORI PROBABILITIES; PARAMETERS ESTIMATIONS; SPARSE MODELS;

MAXIMUM LIKELIHOOD ESTIMATION;

EID: 48149105281     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIAP.2007.4362759     Document Type: Conference Paper

References (28)

1. K. Abou-Moustafa, M. Cheriet, and C. Suen. On the structure of hidden markov models. Pattern Recognition Letters, 25:923-931, 2004.
2. H. Akaike. A new look at the statistical model identification. IEEE Trans. on Automatic Control, AC-19:716-723, 1974.
3. N. Arica and F. Yarman-Vural. A shape descriptor based on circular Hidden Markov Model. In IEEE Proc. Int Conf. Pattern Recognition, volume 1, pages 924-927, 2000.
4. R. Bakis. Continuous speech word recognition via centi-second acoustic states. In Proc. of ASA Meeting, 1976.
5. J. Bernardo and A. Smith. Bayesian Theory. John Wiley & Sons, Chichester (UK), 1994.
6. M. Bicego and V. Murino. Investigating Hidden Markov Models' capabilities in 2D shape classification. IEEE Trans. on Pattern Analysis and Machine Intelligence - PAMI, 26(2):281-286, 2004.
7. M. Bicego, V. Murino, and M. Figueiredo. A sequential pruning strategy for the selection of the number of states in Hidden Markov Models. Pattern Recognition Letters, 24(9-10):1395-1407, 2003.
8. M. Brand. An entropie estimator for structure discovery. In M. Kearns, S. Solla, and D. Cohn, editors, Advances in Neural Information Processing Systems, volume 11. MIT Press, 1999.
9. M. Brand. Structure learning in conditional probability models via an entropie prior and parameter extinction. Neural Computation, 11:1144-1182, 1999.
10. S. Eickeler, A. Kosmala, and G. Rigoll. Hidden Markov Model based continuous online gesture recognition. In IEEE Proc. Int. Conf. Pattern Recognition, volume 2, pages 1206-1208, 1998.
11. M. Figueiredo. Adaptive sparseness for supervised learning. IEEE Trans. on Pattern Analysis and Machine Intelligence, 25(9): 1150-1159, 2003.
12. M. Figueiredo and A. Jain. Unsupervised learning of finite mixture models. IEEE Trans. on Pattern Analysis and Machine Intelligence, 24(3):381-396, 2002.
13. Y. He and A. Kundu. 2-D shape classification using Hidden Markov Model. IEEE Trans. Pattern Analysis Machine Intelligence, 13(11):1172-1184, 1991.
14. J. Hu, M. Brown, and W. Turin. HMM based online hand-writing recognition. IEEE Trans. Pattern Analysis and Machine Intelligence, 18(10): 1039-1045, 1996.
15. R. Hughey and A. Krogh. Hidden Markov Model for sequence analysis: extension and analysis of the basic method. Comp. Appl. in the Biosciences, 12:95-107, 1996.
16. S. Ikeda. Construction of phoneme models - model search of hidden Markov models-, In Proc. Int. Workshop on Intelligent Signal Processing and Communication Systems, pages 82-87, 1993.
17. T. Jebara and A. Pentland. Action reaction learning: Automatic visual analysis and synthesis of interactive behavior. In Proc. Int Conf. Computer Vision Systems, 1999.
18. F. Jelinek. Continuous speech recognition by statistical methods. Proc. of IEEE, 64:532-536, 1976.
19. B. Juang, S. Levinson, and M. Sondhi. Maximum likelihood estimation for multivariate mixture observations of Markov Chain. IEEE Trans. Information Theory, 32(2):307-309, 1986.
20. L. Liporace. Maximum lilelihood estimation for multivariate observations of Markov sources. IEEE Trans. on Information Theory, IT-28(5):729-734, 1982.
21. L. Rabiner. A tutorial on Hidden Markov Models and selected applications in speech recognition. Proc. of IEEE, 77(2):257-286, 1989.
22. J. Rissanen. Stochastic complexity and modeling. The Annals of Statistics, 14:1080-1100, 1986.
23. G. Schwarz. Estimating the dimension of a model. The Annals of Statistics, 6(2):461-4-64, 1978.
24. T. Sebastian, P. Klein, and B. Kimia. Recognition of shapes by editing Shock Graphs. In Proc. Int Conf. Computer Vision, pages 755-762, 2001.
25. H. Singer and M. Ostendorf. Maximum likelihood successive state splitting. In Proc. of IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, volume 2, pages 601-604, 1996.
26. A. Stoicke and S. Omohundro. Hidden Markov Model induction by Bayesian model merging. In S. Hanson, J. Cowan, and C. Giles, editors, Advances in Neural Information Processing Systems, volume 5, pages 11-18. Morgan Kaufmann, San Mateo, CA, 1993.
27. J. Takami and S. Sagayama. A successive state splitting algorithm for efficient allophone modeling. In IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, volume 1, pages 573-576, 1992.
28. V. Vapnik. Statistical Learning Theory. John Wiley, New York, 1998.