H. Akaike, A new look at the statistical model identification problem, IEEE Trans. on Automatic Control, issue.19, pp.716-723, 1974.

H. Attias, A variational Bayesian framework for graphical models, Neural Information Processing Systems (NIPS) Conference, 1999.

S. A. Berrani, L. Amsaleg, and P. Gros, Robust content-based image searches for copyright protection, Proceedings of the first ACM international workshop on Multimedia databases , MMDB 2003, pp.70-77, 2003.
DOI : 10.1145/951676.951690

C. Biernacki, G. Celeux, and G. Govaert, Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models, Computational Statistics & Data Analysis, vol.41, issue.3-4, pp.561-575, 2003.
DOI : 10.1016/S0167-9473(02)00163-9

C. Bishop, Neural networks for Pattern Recognition, 1995.

S. Boyd, A. Ghosh, B. Prabhakar, and D. Shah, Randomized gossip algorithms, IEEE Transactions on Information Theory, vol.52, issue.6, 2006.
DOI : 10.1109/TIT.2006.874516

F. Cozman, M. Cirelo, T. S. Huang, I. Cohen, and N. Sebe, Semisupervised learning of classifiers : theory, algorithms and their applications to human-computer interaction, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.26, issue.12, pp.1553-1567, 2004.

P. T. Eugster, R. Guerraoui, A. Kermarrec, and L. Massoulié, From epidemics to distributed computing, IEEE Computer, vol.37, issue.5, 2003.

R. Fablet, P. Bouthemy, and P. Perez, Nonparametric motion characterization using causal probabilistic models for video indexing and retrieval, IEEE Transactions on Image Processing, vol.11, issue.4, pp.393-407, 2001.
DOI : 10.1109/TIP.2002.999674

J. Goldberger and S. Roweis, Hierarchical clustering of a mixture model, Proc. of Neural Information Processing Systems (NIPS'2004), pp.505-512, 2004.

R. Hammoud and R. Mohr, Gaussian mixture densities for video object recognition, Proc. on Int. Conf. on Pattern Recognition (ICPR'2000), pp.71-75, 2000.

D. Kempe, A. Dobra, and J. Gehrke, Gossip-based computation of aggregate information, 44th Annual IEEE Symposium on Foundations of Computer Science, 2003. Proceedings., 2003.
DOI : 10.1109/SFCS.2003.1238221

D. Mackay, Bayesian Interpolation, Neural Computation, vol.49, issue.3, pp.415-447, 1992.
DOI : 10.1093/comjnl/11.2.185

D. Milojicic, V. Kalogeraki, R. Lukose, L. Nagaraja, J. Pruyne et al., Peer-to-peer computing, 2002.

W. T. Muller, M. Eisenhardt, and A. Henrich, <title>Efficient content-based P2P image retrieval using peer content descriptions</title>, Internet Imaging V, pp.57-68, 2003.
DOI : 10.1117/12.531184

R. Nowak, Distributed EM algorithms for density estimation and clustering in sensor networks, IEEE Transactions on Signal Processing, vol.51, issue.8, 2003.
DOI : 10.1109/TSP.2003.814623

J. Ponce, M. Hebert, C. Schmid, and A. Zisserman, Towards category-level object recognition, 2006.
DOI : 10.1007/11957959

URL : https://hal.archives-ouvertes.fr/inria-00548614

D. Reynolds, Speaker identification and verification using Gaussian mixture speaker models, Speech Communication, vol.17, issue.1-2, pp.91-108, 1995.
DOI : 10.1016/0167-6393(95)00009-D

C. Schmid, Weakly Supervised Learning of Visual Models and Its Application to Content-Based Retrieval, International Journal of Computer Vision, vol.56, issue.1/2, pp.7-16, 2004.
DOI : 10.1023/B:VISI.0000004829.38247.b0

URL : https://hal.archives-ouvertes.fr/inria-00548553

C. Tang, S. Dwarkadas, and Z. Xu, On scaling latent semantic indexing for large peer-to-peer systems, Proceedings of the 27th annual international conference on Research and development in information retrieval , SIGIR '04, pp.145-153, 2004.
DOI : 10.1145/1008992.1009014

J. J. Verbeek, J. R. Nunnink, and N. Vlassis, Accelerated EM-based clustering of large datasets, Data Mining and Knowledge Discovery, 2006.

L. Xie and P. Perez, Slightly supervised learning of part-based appearance models, Proc. of IEEE Workshop of learning in computer vision and pattern recognition, 2004.