On-line em variants for multivariate normal mixture model in background learning and moving foreground detection

Journal of Mathematical Imaging and Vision

Volumn 48, Issue 1, 2014, Pages 114-133

  Li, Dawei ^a   Xu, Lihong ^a   Goodman, Erik ^b  

^a TONGJI UNIVERSITY   (China)

^b MICHIGAN STATE UNIVERSITY   (United States)

Author keywords

Background learning; Fisher information matrix; Foreground detection; Maximum likelihood estimation; Multivariate normal mixture; Newton Raphson; On line EM algorithm

Indexed keywords

DATA STREAMS; E-LEARNING; FISHER INFORMATION MATRIX; ITERATIVE METHODS; MAXIMUM LIKELIHOOD ESTIMATION; MIXTURES; NEWTON-RAPHSON METHOD; NUMERICAL METHODS; REAL TIME SYSTEMS;

BACKGROUND LEARNING; EM ALGORITHMS; FOREGROUND DETECTION; INTELLIGENT LEARNING SYSTEMS; MOVING FOREGROUND DETECTIONS; MULTIVARIATE NORMAL; MULTIVARIATE NORMAL MIXTURE MODEL; SUFFICIENT STATISTICS;

LEARNING SYSTEMS;

EID: 84891846576     PISSN: 09249907     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10851-012-0403-6     Document Type: Article

References (26)

1. Figueiredo, M.A.T., Jain, A.K.: Unsupervised learning of finite mixture models. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 381-396 (2002) (Pubitemid 35289353)
2. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc., Ser. B, Stat. Methodol. 39(1), 1-38 (1977)
3. Wolfe, J.H.: Pattern clustering by multivariate mixture analysis. Multivar. Behav. Res. 5, 329-350 (1970)
4. Titterington, D.M.: Recursive parameter estimation using incomplete data. J. R. Stat. Soc., Ser. B, Methodol. 46(2), 257-267 (1984)
5. Fabian, V.: On asymptotically efficient recursive estimation. Ann. Stat. 6, 854-866 (1978)
6. Zivkovic, Z., van der Heijden, F.: Recursive unsupervised learning of finite mixture models. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 651-656 (2004)
7. Neal, R., Hinton, G.: A view of the EM algorithm that justifies incremental, sparse, and other variants. In: Jordan, M.I. (ed.) Learning in Graphical Models, pp. 355-368. Kluwer Academic, Dordrecht (1998)
8. Cappe, O., Moulines, E.: On-line expectation-maximization algorithm for latent data models. J. R. Stat. Soc., Ser. B, Methodol. 71, 593-613 (2009)
9. Arcidiacono, P., Jones, J.B.: Finite mixture distributions, sequential likelihood and the EM algorithm. Econometrica 71(3), 933-946 (2003)
10. Xu, L., Jordan, M.: On convergence properties of the EM algorithm for Gaussian mixtures. Neural Comput. 8, 129-151 (1996) (Pubitemid 126449919)
11. Redner, R.A., Walker, H.F.: Mixture densities, maximum likelihood, and the EM algorithm. SIAM Rev. 26, 195-239 (1984)
12. Sato, M., Ishii, S.: On-line EM algorithm for the normalized Gaussian network. Neural Comput. 12, 407-432 (2000)
13. Markov, U.E., Smith, A.F.M.: A quasi-Bayes unsupervised learning procedure for priors. IEEE Trans. Inf. Theory 23(6), 761-764 (1977)
14. Smith, A.F.M., Markov, U.E.: A quasi-Bayes sequential procedure for mixtures. J. R. Stat. Soc., Ser. B, Methodol. 40, 106-112 (1978)
15. Wang, S., Zhao, Y.: Almost sure convergence of Titterington's recursive estimator for mixture models. Stat. Probab. Lett. 76, 2001-2006 (2006) (Pubitemid 44550695)
16. Wren, C.R., Azarbayejani, A., Darrell, T., Pentland, A.P.: Pfinder: real-time tracking of the human body. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 780-785 (1997) (Pubitemid 127762271)
17. Stauffer, C., Grimson, W.E.: Adaptive background mixture models for realtime tracking. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit, pp. 246-252 (1999)
18. Stauffer, C., Grimson, W.E.: Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 747-757 (2000)
19. Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction. In: Proc. 17th Int. Conf. Pattern Recognition, pp. 28-31 (2004)
20. Li, D., Xu, L., Goodman, E.: On-line background learning for illumination-robust foreground detection. In: Proc. 11th ICARCV, pp. 1093-1100 (2010)
21. Available at: http://www.cvg.rdg.ac.uk/slides/pets.html
22. Goyette, N., Jodoin, P.-M., Porikli, F., Konrad, J., Ishwar, P.: Changedetection.net: a new change detection benchmark dataset. In: Proc. IEEE Workshop on Change Detection (CDW'12) at CVPR'12, pp. 1-8 (2012)
23. Cheng, J., Yang, J., Zhou, Y., Cui, Y.: Flexible background mixture models for foreground segmentation. Image Vis. Comput. 24, 473-482 (2006) (Pubitemid 43782929)
24. Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Real-time foreground-background segmentation using codebook model. Real-Time Imaging 11(3), 167-256 (2005) (Pubitemid 40929458)
25. Chang, F., Chen, C., Lu, C.: A linear-time component-labeling algorithm using contour tracing technique. Comput. Vis. Image Underst. 93(2), 206-220 (2004)
26. Joo, S., Chellappa, R.: A multiple-hypothesis approach for multiobject visual tracking. IEEE Trans. Image Process. 16(11), 2849-2854 (2007) (Pubitemid 350141857)