S. Alpert, M. Galun, R. Basri, and A. Brandt, Image segmentation by probabilistic bottom-up aggregation and cue integration, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2007.

S. Alpert, M. Galun, A. Brandt, and R. Basri, Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.2, pp.315-327, 2012.
DOI : 10.1109/TPAMI.2011.130

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

F. Calderero and F. Marques, Region Merging Techniques Using Information Theory Statistical Measures, IEEE Transactions on Image Processing, vol.19, issue.6, pp.1567-1586, 2010.
DOI : 10.1109/TIP.2010.2043008

J. Cardelino, V. Caselles, M. Bertalmio, and G. Randall, A Contrario Selection of Optimal Partitions for Image Segmentation, SIAM Journal on Imaging Sciences, vol.6, issue.3, pp.1274-1317, 2013.
DOI : 10.1137/11086029X

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

C. Couprie, C. Farabet, L. Najman, and Y. Lecun, Convolutional nets and watershed cuts for real-time semantic labeling of rgbd video, The Journal of Machine Learning Research, vol.15, pp.3489-3511, 2014.

J. Cousty, G. Bertrand, L. Najman, and M. Couprie, Watershed Cuts: Minimum Spanning Forests and the Drop of Water Principle, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.8, pp.1362-1374, 2009.
DOI : 10.1109/TPAMI.2008.173

URL : https://hal.archives-ouvertes.fr/hal-00622410

J. Cousty, L. Najman, Y. Kenmochi, and S. Guimarães, New Characterizations of Minimum Spanning Trees and of Saliency Maps Based on Quasi-flat Zones
DOI : 10.1007/978-3-319-18720-4_18

URL : https://hal.archives-ouvertes.fr/hal-01148958

J. Cousty, L. Najman, Y. Kenmochi, and S. Guimarães, Hierarchical segmentations with graphs: quasi-flat zones, minimum spanning trees, and saliency maps URL https, 2016.

J. Cousty, L. Najman, and B. Perret, Constructive Links between Some Morphological Hierarchies on Edge-Weighted Graphs, Proceedings of 11th International Symposium on Mathematical Morphology -ISMM 2013, pp.86-97, 2013.
DOI : 10.1007/978-3-642-38294-9_8

URL : https://hal.archives-ouvertes.fr/hal-00798622

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

L. Guigues, J. P. Cocquerez, and H. L. Men, Scale-Sets Image Analysis, International Journal of Computer Vision, vol.20, issue.6, pp.289-317, 2006.
DOI : 10.1007/s11263-005-6299-0

URL : https://hal.archives-ouvertes.fr/hal-00705364

L. Guigues, H. Le-men, and J. P. Cocquerez, The hierarchy of the cocoons of a graph and its application to image segmentation, Pattern Recognition Letters, vol.24, issue.8, pp.1059-1066, 2003.
DOI : 10.1016/S0167-8655(02)00252-0

URL : https://hal.archives-ouvertes.fr/hal-00706166

S. J. Guimarães, J. Cousty, Y. Kenmochi, and L. Najman, A Hierarchical Image Segmentation Algorithm Based on an Observation Scale, pp.116-125, 2012.
DOI : 10.1007/978-3-642-34166-3_13

S. J. Guimarães, Z. K. Do-patrocínio-jr, Y. Kenmochi, J. Cousty, and L. Najman, Hierarchical Image Segmentation Relying on a Likelihood Ratio Test, Image Analysis and Processing -ICIAP 2015 -18th International Conference Proceedings , Part II, pp.25-35978, 2015.
DOI : 10.1007/978-3-319-23234-8_3

Y. Haxhimusa and W. Kropatsch, Hierarchy of Partitions with Dual Graph Contraction, Pattern Recognition, pp.338-345, 2003.
DOI : 10.1007/978-3-540-45243-0_44

Y. Haxhimusa and W. G. Kropatsch, Segmentation Graph Hierarchies, Structural, Syntactic , and Statistical Pattern Recognition, Joint IAPR International Workshops, pp.343-351, 2004.
DOI : 10.1007/978-3-540-27868-9_36

B. R. Kiran and J. Serra, Global???local optimizations by hierarchical cuts and climbing energies, Pattern Recognition, vol.47, issue.1, pp.12-24, 2014.
DOI : 10.1016/j.patcog.2013.05.012

URL : https://hal.archives-ouvertes.fr/hal-00802978

D. R. Martin, An empirical approach to grouping and segmentation, 2003.

D. R. Martin, C. C. Fowlkes, and J. Malik, Learning to detect natural image boundaries using local brightness, color, and texture cues, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.5, pp.530-549, 2004.
DOI : 10.1109/TPAMI.2004.1273918

M. Meil?ameil?a, Comparing clusterings: An axiomatic view, Proceedings of the 22Nd International Conference on Machine Learning, ICML '05, pp.577-584

O. J. Morris, M. J. Lee, and A. G. Constantinides, Graph theory for image analysis: an approach based on the shortest spanning tree, IEE Proceedings F Communications, Radar and Signal Processing, vol.133, issue.2, pp.146-152, 1986.
DOI : 10.1049/ip-f-1.1986.0025

L. Najman, On the Equivalence Between Hierarchical Segmentations and??Ultrametric Watersheds, Journal of Mathematical Imaging and Vision, vol.113, issue.3, pp.231-247, 2011.
DOI : 10.1007/s10851-011-0259-1

URL : https://hal.archives-ouvertes.fr/hal-00419373

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

B. Perret, J. Cousty, J. C. Ura, and S. J. Guimarães, Evaluation of Morphological Hierarchies for Supervised Segmentation, Mathematical Morphology and Its Applications to Signal and Image Processing, pp.39-50, 2015.
DOI : 10.1007/978-3-319-18720-4_4

URL : https://hal.archives-ouvertes.fr/hal-01142072

J. Pont-tuset and F. Marqués, Measures and Meta-Measures for the Supervised Evaluation of Image Segmentation, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013.
DOI : 10.1109/CVPR.2013.277

J. Pont-tuset and F. Marques, Supervised Evaluation of Image Segmentation and Object Proposal Techniques, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.38, issue.7, 2015.
DOI : 10.1109/TPAMI.2015.2481406

C. Rother, V. Kolmogorov, and A. Blake, "GrabCut", ACM Transactions on Graphics, vol.23, issue.3, pp.309-314, 2004.
DOI : 10.1145/1015706.1015720

J. Shi and J. Malik, Normalized cuts and image segmentation, IEEE Trans. Pattern Anal. Mach. Intell, vol.22, issue.8, pp.888-905, 2000.

P. Soille, Constrained connectivity for hierarchical image partitioning and simplification . Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.30, issue.7, pp.1132-1145, 2008.

K. J. De-souza, A. De-albuquerque-araújo, Z. K. Do-patrocínio-jr, and S. J. Guimarães, Graph-based hierarchical video segmentation based on a simple dissimilarity measure, Pattern Recognition Letters, vol.47, pp.85-92, 2014.
DOI : 10.1016/j.patrec.2014.02.016

R. E. Tarjan, Efficiency of a Good But Not Linear Set Union Algorithm, Journal of the ACM, vol.22, issue.2, pp.215-225, 1975.
DOI : 10.1145/321879.321884

R. Unnikrishnan, C. Pantofaru, and M. Hebert, Toward Objective Evaluation of Image Segmentation Algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.6, pp.929-944, 2007.
DOI : 10.1109/TPAMI.2007.1046

D. Varas, M. Alfaro, and F. Marqués, Multiresolution Hierarchy Co-Clustering for Semantic Segmentation in Sequences with Small Variations, 2015 IEEE International Conference on Computer Vision (ICCV), p.4842, 1510.
DOI : 10.1109/ICCV.2015.520

V. Vilaplana, F. Marques, and P. Salembier, Binary Partition Trees for Object Detection, IEEE Transactions on Image Processing, vol.17, issue.11, pp.2201-2216, 2008.
DOI : 10.1109/TIP.2008.2002841

Y. Xu, T. Géraud, and L. Najman, Connected Filtering on Tree-Based Shape-Spaces, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.38, issue.6, pp.1126-1140, 2016.
DOI : 10.1109/TPAMI.2015.2441070

URL : https://hal.archives-ouvertes.fr/hal-01162437

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