A. Ng, M. Jordan, and Y. Weiss, On spectral clustering: Analysis and an algorithm, Adv. Neural Inf. Process. Syst, pp.849-856, 2001.

D. Comaniciu and P. Meer, Mean shift: a robust approach toward feature space analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.5, pp.603-619, 2002.
DOI : 10.1109/34.1000236

P. Felzenszwalb and D. 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

B. Leibe, K. Mikolajczyk, and B. Schiele, Efficient Clustering and Matching for Object Class Recognition, Procedings of the British Machine Vision Conference 2006, 2006.
DOI : 10.5244/C.20.81
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.212.6637

F. Cao, J. Delon, A. Desolneux, P. Musé, and F. Sur, A Unified Framework for Detecting Groups and Application to Shape Recognition, Journal of Mathematical Imaging and Vision, vol.4, issue.4, pp.91-119, 2007.
DOI : 10.1007/s10851-006-9176-0
URL : https://hal.archives-ouvertes.fr/inria-00000360

I. Dhillon, Y. Guan, and B. Kulis, Weighted Graph Cuts without Eigenvectors A Multilevel Approach, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.11, pp.1944-1957, 2007.
DOI : 10.1109/TPAMI.2007.1115

G. Flake, R. Tarjan, and K. , Graph Clustering and Minimum Cut Trees, Internet Mathematics, vol.1, issue.4, pp.385-408, 2004.
DOI : 10.1080/15427951.2004.10129093
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.103.6592

R. Kannan, S. Vempala, and A. Vetta, On clusterings, Journal of the ACM, vol.51, issue.3, pp.497-515, 2004.
DOI : 10.1145/990308.990313

J. Kleinberg, An impossibility theorem for clustering, Adv. Neural Inf. Process. Syst, pp.446-453, 2002.

A. K. Jain, Data clustering: 50 years beyond K-means, Pattern Recognition Letters, vol.31, issue.8, pp.651-666, 2010.
DOI : 10.1016/j.patrec.2009.09.011
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.151.4286

S. Bandyopadhyay, An automatic shape independent clustering technique, Pattern Recognition, vol.37, issue.1, pp.33-45, 2004.
DOI : 10.1016/S0031-3203(03)00235-8

C. Fowlkes, S. Belongie, F. Chung, and J. Malik, Spectral grouping using the nystrom method, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.2, pp.214-225, 2004.
DOI : 10.1109/TPAMI.2004.1262185

J. Shi and J. Malik, Normalized cuts and image segmentation, IEEE Trans. Pattern Anal. and Mach. Intell, vol.22, pp.888-905, 2000.

C. T. Zahn, Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters, IEEE Transactions on Computers, vol.20, issue.1, pp.68-86, 1971.
DOI : 10.1109/T-C.1971.223083

M. Wertheimer, Laws of organization in perceptual forms, Routledge and Kegan Paul, pp.71-88

A. Desolneux, L. Moisan, and J. M. , Edge detection by helmholtz principle, Journal of Mathematical Imaging and Vision, vol.14, issue.3, pp.271-284, 2001.
DOI : 10.1023/A:1011290230196
URL : https://hal.archives-ouvertes.fr/hal-00170793

F. Chung, Spectral Graph Theory (CBMS Regional Conference Series in Mathematics, 1997.

U. Von-luxburg, M. Belkin, and O. Bousquet, Consistency of spectral clustering, The Annals of Statistics, vol.36, issue.2, pp.555-586, 2008.
DOI : 10.1214/009053607000000640

K. Burnham and D. Anderson, Model selection and multimodel inference: a practical information-theoretic approach, 2002.
DOI : 10.1007/b97636

A. Barron, J. Rissanen, and B. Yu, The minimum description length principle in coding and modeling, IEEE Transactions on Information Theory, vol.44, issue.6, pp.2743-2760, 1998.
DOI : 10.1109/18.720554

B. Nadler and M. Galun, Fundamental limitations of spectral clustering, Adv. Neural Inf. Process. Syst, vol.19, pp.1017-1024, 2007.

U. Ozertem, D. Erdogmus, and R. Jenssen, Mean shift spectral clustering, Pattern Recognition, vol.41, issue.6, pp.1924-1938, 2008.
DOI : 10.1016/j.patcog.2007.09.009

A. Desolneux, L. Moisan, and J. M. , From Gestalt Theory to Image Analysis, 2008.
DOI : 10.1007/978-0-387-74378-3
URL : https://hal.archives-ouvertes.fr/hal-00259077

K. Fukunaga, Introduction to Statistical Pattern Recognition, 1999.

R. Hoffman and A. K. Jain, A test of randomness based on the minimal spanning tree, Pattern Recognition Letters, vol.1, issue.3, pp.175-180, 1983.
DOI : 10.1016/0167-8655(83)90059-4

A. K. Jain, X. Xu, T. K. Ho, and F. Xiao, Uniformity testing using minimal spanning tree, Object recognition supported by user interaction for service robots, pp.281-284, 2002.
DOI : 10.1109/ICPR.2002.1047451

L. Zelnik-manor and P. Perona, Self-tuning spectral clustering, Adv. Neural Inf. Process. Syst, vol.17, pp.1601-1608, 2004.

N. Burrus, T. M. Bernard, and J. M. Jolion, Image segmentation by a contrario simulation, Pattern Recognition, vol.42, issue.7, pp.1520-1532, 2009.
DOI : 10.1016/j.patcog.2009.01.003
URL : https://hal.archives-ouvertes.fr/hal-00847910

K. Fukunaga and L. Hostetler, The estimation of the gradient of a density function, with applications in pattern recognition, IEEE Transactions on Information Theory, vol.21, issue.1, pp.32-40, 2003.
DOI : 10.1109/TIT.1975.1055330

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

T. Cour, F. Benezit, and J. Shi, Spectral Segmentation with Multiscale Graph Decomposition, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.1124-1131, 2005.
DOI : 10.1109/CVPR.2005.332

S. Yu and J. Shi, Multiclass spectral clustering, Proceedings Ninth IEEE International Conference on Computer Vision, pp.313-319, 2003.
DOI : 10.1109/ICCV.2003.1238361
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.324.4538

R. Tarjan and J. Van-leeuwen, Worst-case Analysis of Set Union Algorithms, Journal of the ACM, vol.31, issue.2, pp.245-281, 1984.
DOI : 10.1145/62.2160