T. Bdiri and N. Bouguila, An Infinite Mixture of Inverted Dirichlet Distributions, Lecture Notes in Computer Science, vol.42, issue.4, pp.71-78, 2011.
DOI : 10.2307/2986186

T. Bdiri and N. Bouguila, Learning Inverted Dirichlet Mixtures for Positive Data Clustering, RSFDGrC. Lecture Notes in Computer Science, vol.6743, pp.265-272, 2011.
DOI : 10.1007/978-3-642-21881-1_42

T. Bdiri and N. Bouguila, Positive vectors clustering using inverted Dirichlet finite mixture models, Expert Systems with Applications, vol.39, issue.2, pp.1869-1882, 2012.
DOI : 10.1016/j.eswa.2011.08.063

T. Bdiri and N. Bouguila, Bayesian learning of inverted Dirichlet mixtures for SVM kernels generation, Neural Computing and Applications, vol.45, issue.2, pp.1443-1458, 2013.
DOI : 10.1007/s00521-012-1094-z

A. Benitez and S. F. Chang, Semantic knowledge construction from annotated image collections, Proceedings. IEEE International Conference on Multimedia and Expo, pp.205-208, 2002.
DOI : 10.1109/ICME.2002.1035549

J. C. Bezdek, R. J. Hathaway, J. M. Huband, C. Leckie, and K. Ramamohanarao, Approximate clustering in very large relational data, International Journal of Intelligent Systems, vol.11, issue.8, pp.817-841, 2006.
DOI : 10.1002/int.20162

N. Bouguila, W. Elguebaly, T. Washio, E. Suzuki, K. M. Ting et al., On Discrete Data Clustering, PAKDD. Lecture Notes in Computer Science, vol.5012, pp.503-510, 2008.
DOI : 10.1007/978-3-540-68125-0_44

N. Bouguila and D. Ziou, A Nonparametric Bayesian Learning Model: Application to Text and Image Categorization, Lecture Notes in Computer Science, vol.60, issue.2, pp.463-474, 2009.
DOI : 10.1023/B:VISI.0000029664.99615.94

N. Bouguila and D. Ziou, A Dirichlet Process Mixture of Generalized Dirichlet Distributions for Proportional Data Modeling, IEEE Transactions on Neural Networks, vol.21, issue.1, pp.107-122, 2010.
DOI : 10.1109/TNN.2009.2034851

E. Y. Chang, K. Goh, G. Sychay, and G. Wu, CBSA: content-based soft annotation for multimodal image retrieval using bayes point machines, IEEE Transactions on Circuits and Systems for Video Technology, vol.13, issue.1, pp.26-38, 2003.
DOI : 10.1109/TCSVT.2002.808079

W. Chen and G. Feng, Spectral clustering with discriminant cuts. Knowledge-Based Systems, pp.27-37, 2012.

W. Fan, N. Bouguila, and D. Ziou, Unsupervised Hybrid Feature Extraction Selection for High-Dimensional Non-Gaussian Data Clustering with Variational Inference, IEEE Transactions on Knowledge and Data Engineering, vol.25, issue.7, pp.1670-1685, 2013.
DOI : 10.1109/TKDE.2012.101

W. R. Gilks and P. Wild, Algorithm as 287: Adaptive rejection sampling from logconcave density functions, Applied Statistics, vol.42, issue.4, pp.701-709, 1993.

J. He, M. Li, H. J. Zhang, H. Tong, and C. Zhang, Manifold-ranking based image retrieval, Proceedings of the 12th annual ACM international conference on Multimedia , MULTIMEDIA '04, pp.9-16, 2004.
DOI : 10.1145/1027527.1027531

K. Y. Huang, A hybrid particle swarm optimization approach for clustering and classification of datasets, Knowledge-Based Systems, vol.24, issue.3, pp.420-426, 2011.
DOI : 10.1016/j.knosys.2010.12.003

S. Lazebnik, C. Schmid, and J. Ponce, Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 2 (CVPR'06), pp.2169-2178, 2006.
DOI : 10.1109/CVPR.2006.68

URL : https://hal.archives-ouvertes.fr/inria-00548585

L. J. Li and L. Fei-fei, What, where and who? Classifying events by scene and object recognition, 2007 IEEE 11th International Conference on Computer Vision, pp.1-8, 2007.
DOI : 10.1109/ICCV.2007.4408872

G. S. Lingappaiah, On the generalised inverted dirichlet distribution, Demostratio Mathematica, vol.9, issue.3, pp.423-433, 1976.

D. G. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, vol.60, issue.2, pp.91-110, 2004.
DOI : 10.1023/B:VISI.0000029664.99615.94

J. M. Marin and C. P. Robert, Bayesian Core: A Practical Approach to Computational Bayesian Statistics, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00450489

G. Mclachlan and D. Peel, Finite Mixture Models, 2000.
DOI : 10.1002/0471721182

R. M. Neal, Markov chain sampling methods for dirichlet process mixture models, Journal of Computational and Graphical Statistics, vol.9, pp.249-265, 2000.

C. E. Rasmussen, The infinite gaussian mixture model, Advances in Neural Information Processing Systems (NIPS), pp.554-560, 2000.

A. Selinger and R. C. Nelson, A Perceptual Grouping Hierarchy for Appearance-Based 3D Object Recognition, Computer Vision and Image Understanding, vol.76, issue.1, pp.83-92, 1999.
DOI : 10.1006/cviu.1999.0788

L. Spirkovska and M. B. Reid, Higher-order neural networks applied to 2D and 3D object recognition, Machine Learning, vol.29, issue.22, pp.169-199, 1994.
DOI : 10.1007/BF00993276

A. Topchy, M. Law, A. Jain, and A. Fred, Analysis of Consensus Partition in Cluster Ensemble, Fourth IEEE International Conference on Data Mining (ICDM'04), pp.225-232, 2004.
DOI : 10.1109/ICDM.2004.10100

X. J. Wang, W. Y. Ma, G. R. Xue, and X. Li, Multi-model similarity propagation and its application for web image retrieval, Proceedings of the 12th annual ACM international conference on Multimedia , MULTIMEDIA '04, pp.944-951, 2004.
DOI : 10.1145/1027527.1027746