S. Walker, P. Damien, P. Laud, and . Smith, Bayesian Nonparametric Inference for Random Distributions and Related Functions, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.61, issue.3, pp.485-527, 1999.
DOI : 10.1111/1467-9868.00190

R. Neal, Markov chain sampling methods for Dirichlet process mixture models, J. Comput. Graph. Statist, vol.9, pp.249-265, 2000.
DOI : 10.1080/10618600.2000.10474879

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.115.3668

P. Muller and F. Quintana, Nonparametric Bayesian data analysis, Statist. Sci, vol.19, issue.1, pp.95-110, 2004.
DOI : 10.1007/978-3-319-18968-0

C. Antoniak, Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Problems, The Annals of Statistics, vol.2, issue.6, pp.1152-1174, 1974.
DOI : 10.1214/aos/1176342871

D. Blei and M. Jordan, Variational methods for the Dirichlet process, Twenty-first international conference on Machine learning , ICML '04, pp.12-19, 2004.
DOI : 10.1145/1015330.1015439

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.120.150

P. Orbanz and J. Buhmann, Nonparametric Bayesian Image Segmentation, International Journal of Computer Vision, vol.61, issue.3, pp.25-45, 2008.
DOI : 10.1007/s11263-007-0061-0

URL : http://e-collection.ethbib.ethz.ch/show?type=incoll&nr=2483

J. Zhang, The mean field theory in EM procedures for Markov random fields, IEEE Transactions on Signal Processing, vol.40, issue.10, pp.27-40, 1993.
DOI : 10.1109/78.157297

G. Celeux, F. Forbes, and N. Peyrard, EM procedures using mean field-like approximations for Markov model-based image segmentation, Pattern Recognition, vol.36, issue.1, pp.131-144, 2003.
DOI : 10.1016/S0031-3203(02)00027-4

URL : https://hal.archives-ouvertes.fr/inria-00072526

S. P. Chatzis and G. Tsechpenakis, The infinite hidden Markov random field model, Proc. 12th International IEEE Conference on Computer Vision (ICCV), pp.654-661, 2009.
DOI : 10.1109/iccv.2009.5459177

S. P. Chatzis and G. Tsechpenakis, The Infinite Hidden Markov Random Field Model, IEEE Transactions on Neural Networks, vol.21, issue.6, pp.1004-1014, 2010.
DOI : 10.1109/TNN.2010.2046910

T. Ferguson, A Bayesian Analysis of Some Nonparametric Problems, The Annals of Statistics, vol.1, issue.2, pp.209-230, 1973.
DOI : 10.1214/aos/1176342360

D. Blackwell and J. Macqueen, Ferguson Distributions Via Polya Urn Schemes, The Annals of Statistics, vol.1, issue.2, pp.353-355, 1973.
DOI : 10.1214/aos/1176342372

URL : http://projecteuclid.org/download/pdf_1/euclid.aos/1176342372

J. Maroquin, S. Mitte, and T. Poggio, Probabilistic Solution of Ill-Posed Problems in Computational Vision, Journal of the American Statistical Association, vol.18, issue.397, pp.76-89, 1987.
DOI : 10.1080/01621459.1987.10478393

S. Geman and D. Geman, Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.6, pp.721-741, 1984.

P. Clifford, Markov random fields in statistics Disorder in physical systems. A volume in honour of John M. Hammersley on the occasion of his 70th birthday, 1990.

S. P. Chatzis and T. A. Varvarigou, A Fuzzy Clustering Approach Toward Hidden Markov Random Field Models for Enhanced Spatially Constrained Image Segmentation, IEEE Transactions on Fuzzy Systems, vol.16, issue.5, pp.1351-1361, 2008.
DOI : 10.1109/TFUZZ.2008.2005008

URL : http://dspace.lib.ntua.gr/handle/123456789/18534

W. Qian and D. Titterington, Estimation of Parameters in Hidden Markov Models, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.337, issue.1647, pp.407-428, 1991.
DOI : 10.1098/rsta.1991.0132

D. M. Blei and M. I. Jordan, Variational inference for Dirichlet process mixtures, Bayesian Analysis, vol.1, issue.1, pp.121-144, 2006.
DOI : 10.1214/06-BA104

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.130.9916

M. Jordan, Z. Ghahramani, T. Jaakkola, and L. Saul, An Introduction to Variational Methods for Graphical Models, Learning in Graphical Models. Kluwer, pp.105-162, 1998.
DOI : 10.1007/978-94-011-5014-9_5

D. Martin, C. Fowlkes, D. Tal, and J. Malik, A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, pp.416-423, 2001.
DOI : 10.1109/ICCV.2001.937655

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.34-41, 2005.
DOI : 10.1109/CVPR.2005.390

G. Mori, Guiding model search using segmentation, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 2005.
DOI : 10.1109/ICCV.2005.112

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.60.3944

M. Varma and A. Zisserman, Classifying images of materials: Achieving view-point and illumination independence, Proc. 7th IEEE European Conf. on Computer Vision (ECCV, 2002.

C. Nikou, N. Galatsanos, and A. Likas, A Class-Adaptive Spatially Variant Mixture Model for Image Segmentation, IEEE Transactions on Image Processing, vol.16, issue.4, pp.1121-1130, 2007.
DOI : 10.1109/TIP.2007.891771