MML-based approach for finite dirichlet mixture estimation and selection

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3587 LNAI, Issue , 2005, Pages 42-51

  Bouguila, Nizar ^a   Ziou, Djemel ^a  

^a UNIVERSITÉ DE SHERBROOKE   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE PROCESSING; LEARNING SYSTEMS; MATHEMATICAL MODELS; PROBLEM SOLVING; TEXTURES;

BAYESIAN METHOD; FINITE DIRICHLET MIXTURE MODEL; LEARNING PROBLEMS; MINIMUM MESSAGE LENGTH (MML) PRINCIPLE;

LEARNING ALGORITHMS;

EID: 26944454768     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11510888_5     Document Type: Conference Paper

References (16)

1. A. K. Jain, R. P. W. Duin and J. Mao. Statistical Pattern Recognition: A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1):4-37, January 2000.
2. G.J. McLachlan and D. Peel. Finite Mixture Models. New York: Wiley, 2000.
3. C.S Wallace and D.M. Boulton. An Information Measure for Classification. Computer-Journal, 11 (2):195-209, 1968.
4. A. H. Akaike. A New Look at the Statistical Model Identification. IEEE Transaction on Automatic Control, AC-19(6):716-723, 1974.
5. J. Rissanen. Modeling by Shortest Data Description. Automatica, 14:465-471, 1987.
6. M. A. T. Frigueiredo, J. M. N. Leitao and A. K. Jain. On Fitting Mixture Models. In E. Hancock and M. Pellilo, editors, Energy Minimization Methods in Computer Vision and Pattern Recognition, pages 54-09, 1999.
7. J.C. Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, 1981.
8. S.J. Roberts, D. Husmeier, I. Rezek and W. Penny. Bayesian Approaches to Gaussian Mixture Modeling. IEEE Transactions on PAMI, 20(11):1133-1142, November 1998.
9. R.A Baxter. Minimum Message Length Inference: Theory and Applications. Ph.D. Thesis, Monash University, Clayton, Victoria, Australia, 1996.
10. D. Ziou and N. Bouguila, Unsupervised Learning of a Gamma Finite Mixture Using MML: Application to SAR Image Analysis. In 17th International Conference on Pattern Recognition, ICPR2004, pages 280-283, 2004.
11. N. Bouguila, D. Ziou and J. Vaillancourt. Unsupervised Learning of a Finite Mixture Model Based on the Dirichlet Distribution and its Application. IEEE Transactions on Image Processing, 13(11):1533-1543, November 2004.
12. N. Bouguila, D. Ziou, and J. Vaillancourt. Novel Mixtures Based on the Dirichlet Distribution: Application to Data and Image Classification. In Petra Perner and Azriel Rosenfeld, editors, Machine Learning and Data Mining in Pattern Recognition, pages 172-181, 2003.
13. D.L. Dowe and G. Farr. An Introduction to MML Inference. Technical report, Department of Computer Science, Monash University, 1997.
14. F. A. Graybill. Matrices with applications in Statistics. Wadsworth, California, 1983.
15. R. A. Baxter and J. J. Olivier. Finding Overlapping Components with MML. Statistics and Computing, 10():5-16, 2000.
16. R. M. Haralick, K. Shanmugari and I. Dinstein. Texture features for image classification. IEEE Transactions on Systems, Man and Cybernetics, 8:610-621, 1973.