R. Szeliski, Computer vision: algorithms and applications, 2011.
DOI : 10.1007/978-1-84882-935-0

K. P. Murphy, Machine learning: a probabilistic perspective, 2012.

R. Nock and F. Nielsen, Statistical region merging, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.11, pp.1452-1458, 2004.
DOI : 10.1109/TPAMI.2004.110

Z. Zhang, Microsoft Kinect Sensor and Its Effect, IEEE Multimedia, vol.19, issue.2, pp.4-10, 2012.
DOI : 10.1109/MMUL.2012.24

J. Han, L. Shao, D. Xu, and J. Shotton, Enhanced computer vision with Microsoft Kinect sensor: A review, IEEE Transactions on Cybernetics, vol.43, pp.1318-1334, 2013.

S. Gupta, P. Arbelaez, and J. Malik, Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.564-571, 2013.
DOI : 10.1109/CVPR.2013.79

C. J. Taylor and A. Cowley, Parsing indoor scenes using RGB-D imagery Robotics: Science and Systems VIII, pp.401-408, 2013.
DOI : 10.15607/rss.2012.viii.051
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.6997

N. Silberman, D. Hoiem, P. Kohli, and R. Fergus, Indoor Segmentation and Support Inference from RGBD Images, Computer Vision?ECCV 2012, pp.746-760, 2012.
DOI : 10.1007/978-3-642-33715-4_54

X. Ren, L. Bo, and D. Fox, Rgb-(d) scene labeling: Features and algorithms, IEEE Conference on Computer Vision and Pattern Recognition (CVPR, pp.2759-2766, 2012.

C. Dal-mutto, P. Zanuttigh, and G. M. Cortelazzo, Fusion of Geometry and Color Information for Scene Segmentation, IEEE Journal of Selected Topics in Signal Processing, vol.6, issue.5, pp.505-521, 2012.
DOI : 10.1109/JSTSP.2012.2194474

H. S. Koppula, A. Anand, T. Joachims, and A. Saxena, Semantic labeling of 3D point clouds for indoor scenes, NIPS, p.4, 2011.

H. Zhang, The optimality of naive bayes, AA, vol.1, issue.2, pp.3-13, 2004.

D. D. Lewis, Naive (Bayes) at forty: The independence assumption in information retrieval, Machine learning: ECML-98, pp.4-15, 1998.
DOI : 10.1007/BFb0026666

M. A. Hasnat, O. Alata, and A. Trémeau, Model-based hierarchical clustering with Bregman divergences and Fishers mixture model: application to depth image analysis, Statistics and Computing, vol.4, issue.2, 2015.
DOI : 10.1007/s11222-015-9576-3
URL : https://hal.archives-ouvertes.fr/hal-01220515

M. Liu, B. C. Vemuri, S. Amari, and F. Nielsen, Shape retrieval using hierarchical total Bregman soft clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.12, pp.2407-2419, 2012.

F. Nielsen and V. Garcia, Statistical exponential families: A digest with flash cards, 2009.

C. Fraley and A. E. Raftery, How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis, The Computer Journal, vol.41, issue.8, pp.578-588, 1998.
DOI : 10.1093/comjnl/41.8.578

C. Biernacki, G. Celeux, and G. Govaert, Assessing a mixture model for clustering with the integrated completed likelihood, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.7, pp.719-725, 2000.
DOI : 10.1109/34.865189

C. Fraley and A. E. Raftery, Model-based methods of classification: using the mclust software in chemometrics, Journal of Statistical Software, vol.18, issue.6, pp.1-13, 2007.

M. A. Figueiredo and A. K. Jain, Unsupervised learning of finite mixture models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.3, pp.381-396, 2002.
DOI : 10.1109/34.990138

Y. Ma, H. Derksen, W. Hong, and J. Wright, Segmentation of Multivariate Mixed Data via Lossy Data Coding and Compression, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.9, pp.1546-1562, 2007.
DOI : 10.1109/TPAMI.2007.1085

O. Alata and L. Quintard, Is there a best color space for color image characterization or representation based on Multivariate Gaussian Mixture Model?, Computer Vision and Image Understanding, vol.113, issue.8, pp.867-877, 2009.
DOI : 10.1016/j.cviu.2009.03.001
URL : https://hal.archives-ouvertes.fr/hal-00438254

V. Garcia and F. Nielsen, Simplification and hierarchical representations of mixtures of exponential families, Signal Processing, vol.90, issue.12, pp.3197-3212, 2010.
DOI : 10.1016/j.sigpro.2010.05.024

T. M. Nguyen and Q. Wu, Fast and Robust Spatially Constrained Gaussian Mixture Model for Image Segmentation, IEEE transactions on circuits and systems for video technology, pp.621-635, 2013.
DOI : 10.1109/TCSVT.2012.2211176

G. J. Mclachlan and T. Krishnan, The EM algorithm and extensions, ser. Wiley series in probability and statistics, 2008.

C. M. Bishop, Pattern recognition and machine learning, 2006.

A. Banerjee, S. Merugu, I. S. Dhillon, and J. Ghosh, Clustering with Bregman Divergences, The Journal of Machine Learning Research, vol.6, pp.1705-1749, 2005.
DOI : 10.1137/1.9781611972740.22

A. Trémeau and P. Colantoni, Regions adjacency graph applied to color image segmentation, IEEE Transactions on Image Processing, vol.9, issue.4, pp.735-744, 2000.
DOI : 10.1109/83.841950

B. Peng and D. Zhang, Automatic Image Segmentation by Dynamic Region Merging, IEEE Transactions on Image Processing, vol.20, issue.12, pp.3592-3605, 2011.
DOI : 10.1109/TIP.2011.2157512

A. Martínez-usóus´usó, F. Pla, and P. García-sevilla, Unsupervised colour image segmentation by low-level perceptual grouping, Pattern Analysis and Applications, vol.32, issue.200, pp.581-594, 2013.
DOI : 10.1007/s10044-011-0259-1

R. B. Rusu, Semantic 3D Object Maps for Everyday Robot Manipulation, 2013.
DOI : 10.1007/978-3-642-35479-3

C. J. Taylor and A. Cowley, Segmentation and analysis of RGB-D data, RSS 2011 Workshop on RGB-D Cameras, 2011.

P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik, Contour Detection and Hierarchical Image Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.5, pp.898-916, 2011.
DOI : 10.1109/TPAMI.2010.161

J. Freixenet, X. Muñozmu?muñoz, D. Raba, J. Martí, and X. Cufí, Yet Another Survey on Image Segmentation: Region and Boundary Information Integration, Computer VisionECCV, pp.408-422, 2002.
DOI : 10.1007/3-540-47977-5_27

J. Boissonnat, F. Nielsen, and R. Nock, Bregman Voronoi Diagrams, Discrete & Computational Geometry, vol.12, issue.2, pp.281-307, 2010.
DOI : 10.1007/s00454-010-9256-1
URL : https://hal.archives-ouvertes.fr/hal-00488441

M. A. Hasnat, O. Alata, and A. Trémeau, Hierarchical 3-d von Mises-Fisher mixture model, 1st Workshop on Divergences and Divergence Learning (WDDL), 2013.

C. Dal-mutto, P. Zanuttigh, and G. M. Cortelazzo, Time-of-flight cameras and microsoft Kinect, 2012.
DOI : 10.1007/978-1-4614-3807-6

D. Holz, S. Holzer, R. B. Rusu, and S. Behnke, Real-Time Plane Segmentation Using RGB-D Cameras, RoboCup 2011: Robot Soccer World Cup XV, pp.306-317, 2012.
DOI : 10.1007/978-3-642-32060-6_26

P. F. Felzenszwalb and D. P. Huttenlocher, Efficient Graph-Based Image Segmentation, International Journal of Computer Vision, vol.59, issue.2, pp.167-181, 2004.
DOI : 10.1023/B:VISI.0000022288.19776.77

Y. Niu, Y. Geng, X. Li, and F. Liu, Leveraging stereopsis for saliency analysis, IEEE Conference on Computer Vision and Pattern Recognition (CVPR, pp.454-461, 2012.

J. Strom, A. Richardson, and E. Olson, Graph-based segmentation for colored 3D laser point clouds, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.2131-2136, 2010.
DOI : 10.1109/IROS.2010.5650459

J. T. Barron and J. Malik, Intrinsic scene properties from a single RGB-D image, IEEE Conference on Computer Vision and Pattern Recognition (CVPR, pp.17-24, 2013.

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

. Hu, Global contrast based salient region detection, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.409-416, 2011.