R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua et al., SLIC superpixels compared to state-of-the-art superpixel methods, IEEE Trans. Pattern Anal. Mach. Intell, vol.34, issue.11, pp.2274-2282, 2012.

M. Albughdadi, L. Chaâri, J. Tourneret, F. Forbes, and P. Ciuciu, A Bayesian non-parametric hidden Markov random model for hemodynamic brain parcellation, Signal Processing, vol.135, pp.132-146, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01426385

P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik, Contour detection and hierarchical image segmentation, IEEE Trans. Pattern Anal. Mach. Intell, vol.33, issue.5, pp.898-916, 2011.

D. Arthur and S. Vassilvitskii, K-means++: The advantages of careful seeding, Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA '07, pp.1027-1035, 2007.

M. Beal and Z. Ghahramani, The variational Bayesian EM Algorithm for incomplete data: with application to scoring graphical model structures, Bayesian Statistics, pp.453-464, 2003.

J. Besag, Spatial interaction and the statistical analysis of lattice systems, Journal of the Royal Statistical Society. Series B (Methodological), pp.192-236, 1974.

D. M. Blei and M. I. Jordan, Variational inference for Dirichlet process mixtures, Bayesian Anal, vol.1, issue.1, pp.121-143, 2006.

G. Celeux, F. Forbes, and N. Peyrard, EM procedures using mean field-like approximations for Markov model-based image segmentation, Pattern Recognition, vol.36, pp.131-144, 2003.
URL : https://hal.archives-ouvertes.fr/inria-00072526

L. Chaari, T. Vincent, F. Forbes, M. Dojat, and P. Ciuciu, Fast joint detection-estimation of evoked brain activity in event-related fmri using a variational approach, IEEE Trans. Med. Imag, vol.32, issue.5, pp.821-837, 2013.
URL : https://hal.archives-ouvertes.fr/inserm-00753873

D. Chandler, Introduction to modern statistical mechanics, 1987.

S. P. Chatzis, A Markov random field-regulated Pitman-Yor process prior for spatially constrained data clustering, Pattern Recognition, vol.46, issue.6, pp.1595-1603, 2013.

S. P. Chatzis and G. Tsechpenakis, The infinite hidden Markov random field model, IEEE Trans. Neural Networks, vol.21, issue.6, pp.1004-1014, 2010.

A. Corduneanu and C. M. Bishop, Variational Bayesian Model Selection for Mixture Distributions, Proceedings Eighth International Conference on Artificial Intelligence and Statistics, pp.27-34, 2001.

P. De-blasi, S. Favaro, A. Lijoi, R. H. Mena, I. Prünster et al., Are Gibbs-type priors the most natural generalization of the Dirichlet process?, IEEE Trans. Pattern Anal. Mach. Intell, vol.37, issue.2, pp.212-229, 2015.

S. Favaro, A. Lijoi, C. Nava, B. Nipoti, I. Prünster et al., On the Stick-Breaking Representation for Homogeneous NRMIs, Bayesian Anal, vol.11, issue.3, pp.697-724, 2016.

T. S. Ferguson, A Bayesian analysis of some nonparametric problems. The Annals of Statistics pp, pp.209-230, 1973.

F. Forbes and N. Peyrard, Hidden Markov Random Field model selection criteria based on mean field-like approximations, IEEE Trans. Pattern Anal. Mach. Intell, vol.25, issue.9, pp.1089-1101, 2003.

S. Ghosal and A. Van-der-vaart, Fundamentals of nonparametric Bayesian inference, vol.44, 2017.

P. Green, Reversible jump Markov chain Monte Carlo computation and Bayesian model determination, Biometrika, vol.82, pp.711-732, 1995.

H. Ishwaran and L. F. James, Gibbs sampling methods for stick-breaking priors, Journal of the American Statistical Association, vol.96, issue.453, pp.161-173, 2001.

T. D. Johnson, Z. Liu, A. J. Bartsch, and T. E. Nichols, A Bayesian non-parametric Potts model with application to pre-surgical fMRI data, Statistical Methods in Medical Research, vol.22, issue.4, pp.364-381, 2013.

G. Mclachlan and T. Krishnan, The EM Algorithm and Extensions, 1996.

J. W. Miller and M. T. Harrison, Mixture models with a prior on the number of components, Journal of the American Statistical Association, vol.113, pp.340-356, 2018.

K. P. Murphy, Conjugate bayesian analysis of the gaussian distribution, def, vol.1, issue.2?2, p.16, 2007.

R. M. Neal and G. E. Hinton, A view of the EM algorithm that justifies incremental, sparse and other variants, Jordan (ed.) Lear. in Graph. Mod, pp.355-368, 1998.

P. Orbanz and J. M. Buhmann, Nonparametric Bayesian image segmentation, International Journal of Computer Vision, vol.77, issue.1-3, pp.25-45, 2008.

J. Pitman, Combinatorial stochastic processes, Lectures from the 32nd Summer School on Probability Theory, vol.1875, 2002.

J. Pitman and M. Yor, The Two-Parameter Poisson-Dirichlet Distribution Derived from a Stable Subordinator, The Annals of Probability, vol.25, issue.2, pp.855-900, 1997.

W. M. Rand, Objective criteria for the evaluation of clustering methods, Journal of the American Statistical Association, vol.66, issue.336, pp.846-850, 1971.

A. R. Da-silva, A Dirichlet process mixture model for brain MRI tissue classification, Medical Image Analysis, vol.11, issue.2, pp.169-182, 2007.

J. Sodjo, A. Giremus, N. Dobigeon, and J. F. Giovannelli, A generalized Swendsen-Wang algorithm for Bayesian nonparametric joint segmentation of multiple images, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.1882-1886, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01695104

J. Stoehr, A review on statistical inference methods for discrete Markov random fields, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01462078

E. B. Sudderth and M. I. Jordan, Shared segmentation of natural scenes using dependent Pitman-Yor processes, Advances in Neural Information Processing Systems 21, Proceedings of the Twenty-Second Annual Conference on Neural Information Processing Systems, pp.1585-1592, 2008.

Y. W. Teh, A Bayesian interpretation of interpolated Kneser-Ney, 2006.

R. Unnikrishnan, C. Pantofaru, and M. Hebert, A measure for objective evaluation of image segmentation algorithms, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) -Workshops, pp.34-34, 2005.

M. Varma and A. Zisserman, A statistical approach to texture classification from single images, International Journal of Computer Vision, vol.62, issue.1, pp.61-81, 2005.

C. Wang and D. M. Blei, Truncation-free stochastic variational inference for Bayesian nonparametric models, Proceedings of the 25th International Conference on Neural Information Processing Systems, vol.1, pp.413-421, 2012.

D. Xu, F. Caron, and A. Doucet, Bayesian nonparametric image segmentation using a generalized Swendsen-Wang algorithm, 2016.