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, pp.315-327, 2011.

J. Angulo and D. Jeulin, Stochastic watershed segmentation, ISMM, 2007.
URL : https://hal.archives-ouvertes.fr/hal-01134047

J. Angulo and J. Serra, Automatic analysis of dna microarray images using mathematical morphology, Bioinformatics, vol.19, issue.5, pp.553-562, 2003.

J. Angulo, S. Velasco-forero, and J. Chanussot, Multiscale stochastic watershed for unsupervised hyperspectral image segmentation, IEEE International Geoscience and Remote Sensing Symposium, vol.3, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00449454

P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik, Contour detection and hierarchical image segmentation, IEEE PAMI, vol.33, issue.5, pp.898-916, 2011.

P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik, Contour detection and hierarchical image segmentation, IEEE PAMI, vol.33, issue.5, pp.898-916, 2011.

P. Arbeláez, J. Pont-tuset, J. T. Barron, F. Marques, and J. Malik, Multiscale combinatorial grouping, Proceedings IEEE CVPR, pp.328-335, 2014.

R. Audigier and R. Lotufo, Seed-relative segmentation robustness of watershed and fuzzy connectedness approaches, XX Brazilian Symposium on Computer Graphics and Image Processing, pp.61-70, 2007.

R. Audigier and R. Lotufo, Uniquely-determined thinning of the tie-zone watershed based on label frequency, JMIV, vol.27, issue.2, pp.157-173, 2007.

R. Audigier and R. D. Lotufo, Watershed by image foresting transform, tiezone, and theoretical relationships with other watershed definitions, Mathematical 163 References Morphology and its Applications to Signal and Image Processing (ISMM), pp.277-288, 2007.

J. A. Benediktsson, L. Bruzzone, J. Chanussot, M. Mura, P. Salembier et al., Hierarchical analysis of remote sensing data: Morphological attribute profiles and binary partition trees, International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing, pp.306-319
URL : https://hal.archives-ouvertes.fr/hal-00696056

. Springer, , 2011.

S. Beucher, Use of watersheds in contour detection, Proceedings of the International Workshop on Image Processing. CCETT, 1979.

S. Beucher, Watershed, hierarchical segmentation and waterfall algorithm, Mathematical morphology and its applications to image processing, pp.69-76

. Springer, , 1994.

S. Beucher and F. Meyer, The morphological approach to segmentation: the watershed transformation, Optical Engineering-New York-Marcel Dekker Incorporated, vol.34, pp.433-433, 1992.

S. Beucher and F. Meyer, The morphological approach to segmentation: the watershed transformation. Mathematical morphology in image processing, vol.34, pp.433-481, 1993.

A. Blake, C. Rother, M. Brown, P. Perez, and P. Torr, Interactive image segmentation using an adaptive gmmrf model, European conference on computer vision, pp.428-441, 2004.

Y. Boykov, O. Veksler, and R. Zabih, Fast approximate energy minimization via graph cuts, Proceedings of the Seventh IEEE International Conference on Computer Vision, vol.1, pp.377-384, 1999.

E. J. Breen and R. Jones, Attribute openings, thinnings, and granulometries. Computer Vision and Image Understanding, vol.64, pp.377-389, 1996.

J. Chaussard, M. Couprie, and H. Talbot, Robust skeletonization using the discrete ?-medial axis, Pattern Recognition Letters, vol.32, issue.9, pp.1384-1394, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00622523

C. Couprie, L. Grady, L. Najman, and H. Talbot, Power watersheds: A new image segmentation framework extending graph cuts, random walker and optimal spanning forest, ICCV, pp.731-738, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00622409

C. Couprie, L. Grady, L. Najman, and H. Talbot, Power watershed: A unifying graph-based optimization framework, IEEE transactions on pattern analysis and machine intelligence, vol.33, pp.1384-1399, 2010.

J. Cousty, G. Bertrand, L. Najman, and M. Couprie, Watershed cuts: Minimum spanning forests and the drop of water principle, IEEE PAMI, vol.31, issue.8, pp.1362-1374, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00622410

J. Cousty, G. Bertrand, L. Najman, and M. Couprie, Watershed cuts: Thinnings, shortest path forests, and topological watersheds, IEEE PAMI, vol.32, issue.5, pp.925-939, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00729346

J. Cousty and L. Najman, Incremental algorithm for hierarchical minimum spanning forests and saliency of watershed cuts, ISMM, pp.272-283, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00622505

J. Cousty, L. Najman, Y. Kenmochi, and S. Guimarães, Hierarchical segmentations with graphs: quasi-flat zones, minimum spanning trees, and saliency maps, JMIV, vol.60, issue.4, pp.479-502, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01344727

J. Cousty, L. Najman, Y. Kenmochi, and S. J. Guimarães, Hierarchical segmentations with graphs: quasi-flat zones, minimum spanning trees, and saliency maps, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01344727

J. Cousty, L. Najman, and B. Perret, Constructive links between some morphological hierarchies on edge-weighted graphs, ISMM, pp.86-97, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00798622

J. Cousty, L. Najman, and J. Serra, Raising in watershed lattices, 15th IEEE ICIP, pp.2196-2199, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00622472

P. Dollár and C. L. Zitnick, Fast edge detection using structured forests, IEEE transactions on pattern analysis and machine intelligence, vol.37, pp.1558-1570, 2014.

L. Euler, Solutio problematis ad geometriam situs pertinentis. Commentarii academiae scientiarum Petropolitanae, pp.128-140, 1741.

A. X. Falcão, J. Stolfi, R. De-alencar, and . Lotufo, The image foresting transform: Theory, algorithms, and applications. IEEE transactions on pattern analysis and machine intelligence, vol.26, pp.19-29, 2004.

C. Farabet, C. Couprie, L. Najman, and Y. Lecun, Learning hierarchical features for scene labeling, IEEE transactions on pattern analysis and machine intelligence, vol.35, pp.1915-1929, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00742077

A. Fehri, S. Velasco-forero, and F. Meyer, Automatic selection of stochastic watershed hierarchies, EUSIPCO, pp.1877-1881, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01361512

A. Fehri, S. Velasco-forero, and F. Meyer, Characterizing images by the gromovhausdorff distances between derived hierarchies, 25th IEEE International Conference on Image Processing (ICIP), pp.1213-1217, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01823866

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.

P. J. Figueroa, N. J. Leite, and R. M. Barros, A flexible software for tracking of markers used in human motion analysis. Computer methods and programs in biomedicine, vol.72, pp.155-165, 2003.

J. M. Gauch, Image segmentation and analysis via multiscale gradient watershed hierarchies, IEEE transactions on image processing, vol.8, issue.1, pp.69-79, 1999.

D. M. Gavrila and V. Philomin, Real-time object detection for" smart" vehicles, Proceedings of the Seventh IEEE International Conference on Computer Vision, vol.1, pp.87-93, 1999.

T. Géraud, E. Carlinet, S. Crozet, and L. Najman, A quasi-linear algorithm to compute the tree of shapes of nd images, International symposium on mathematical morphology and its applications to signal and image processing, pp.98-110, 2013.

P. Ghamisi, B. Höfle, and X. X. Zhu, Hyperspectral and lidar data fusion using extinction profiles and deep convolutional neural network, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.10, issue.6, pp.3011-3024, 2016.

P. Ghamisi, R. Souza, J. A. Benediktsson, X. X. Zhu, L. Rittner et al., Extinction profiles for the classification of remote sensing data, IEEE Transactions on Geoscience and Remote Sensing, vol.54, issue.10, pp.5631-5645, 2016.

J. C. Gower and G. J. Ross, Minimum spanning trees and single linkage cluster analysis, Journal of the Royal Statistical Society: Series C (Applied Statistics), vol.18, issue.1, pp.54-64, 1969.

L. Grady, Random walks for image segmentation, IEEE Transactions on Pattern Analysis & Machine Intelligence, issue.11, pp.1768-1783, 2006.

V. Grau, A. Mewes, M. Alcaniz, R. Kikinis, and S. K. Warfield, Improved watershed transform for medical image segmentation using prior information, IEEE transactions on medical imaging, vol.23, issue.4, pp.447-458, 2004.

M. Grimaud, New measure of contrast: the dynamics, Image Algebra and Morphological Image Processing III, vol.1769, pp.292-306, 1992.

M. Grundmann, V. Kwatra, M. Han, and I. Essa, Efficient hierarchical graph-based video segmentation, 2010 ieee computer society conference on computer vision and pattern recognition, pp.2141-2148, 2010.

S. Guimarães, Y. Kenmochi, J. Cousty, Z. Patrocinio, and L. Najman, Hierarchizing graph-based image segmentation algorithms relying on region dissimilarity, Mathematical Morphology-Theory and Applications, vol.2, issue.1, pp.55-75, 2017.

S. J. Guimarães, J. Cousty, Y. Kenmochi, and L. Najman, A hierarchical image segmentation algorithm based on an observation scale, Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), pp.116-125, 2012.

S. Houben, J. Stallkamp, J. Salmen, M. Schlipsing, and C. Igel, Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark, International Joint Conference on Neural Networks, p.1288, 2013.

Q. Huang and B. Dom, Quantitative methods of evaluating image segmentation, Proceedings., International Conference on Image Processing, vol.3, pp.53-56, 1995.

D. Jeulin, Morphological probabilistic hierarchies for texture segmentation, Mathematical Morphology-Theory and Applications, vol.1, issue.1, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01126741

B. R. Kiran and J. Serra, Fusion of ground truths and hierarchies of segmentations, PRL, vol.47, pp.63-71, 2014.

B. R. Kiran and J. Serra, Braids of partitions, International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing, pp.217-228, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01134114

T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona et al., Microsoft coco: Common objects in context, European conference on computer vision, pp.740-755, 2014.

R. Lotufo and W. Silva, Minimal set of markers for the watershed transform, Proceedings of ISMM, vol.2002, pp.359-368, 2002.

V. Machairas, M. Faessel, D. Cárdenas-peña, T. Chabardes, T. Walter et al., Waterpixels. IEEE Transactions on Image Processing, vol.24, pp.3707-3716, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01212760

D. S. Maia, A. D. Araujo, J. Cousty, L. Najman, B. Perret et al., Evaluation of combinations of watershed hierarchies, ISMM, pp.133-145
URL : https://hal.archives-ouvertes.fr/hal-01552420

. Springer, , 2017.

K. Maninis, J. Pont-tuset, P. Arbeláez, and L. Van-gool, Convolutional oriented boundaries, ECCV, pp.580-596, 2016.

K. Maninis, J. Pont-tuset, P. Arbeláez, and L. Van-gool, Convolutional oriented boundaries: From image segmentation to high-level tasks, IEEE transactions on pattern analysis and machine intelligence, vol.40, pp.819-833, 2017.

K. Maninis, J. Pont-tuset, P. Arbeláez, and L. Van-gool, Convolutional oriented boundaries: From image segmentation to high-level tasks, PAMI, vol.40, issue.4, pp.819-833, 2018.

B. Marcotegui, Residual approach on a hierarchical segmentation, 2014 IEEE International Conference on Image Processing (ICIP), pp.4353-4357, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01080417

B. Marcotegui, F. Zanoguera, P. Correia, R. Rosa, F. Marqués et al., A video object generation tool allowing friendly user interaction, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348), vol.2, pp.391-395, 1999.

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, Proc. 8th Int'l Conf. Computer Vision, vol.2, pp.416-423, 2001.

D. R. Martin, An Empirical Approach to Grouping and Segmentation, 2003.

E. Meinhardt, Morphological and Statistical Techniques for the Analysis of 3D Images, 2010.

F. Meyer, Minimum spanning forests for morphological segmentation, Mathematical morphology and its applications to image processing, pp.77-84, 1994.

F. Meyer, The dynamics of minima and contours, ISMM, pp.329-336, 1996.

F. Meyer, Watersheds on weighted graphs, Pattern Recognition Letters, vol.47, pp.72-79, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01111752

F. Meyer and P. Maragos, Morphological scale-space representation with levelings, International Conference on Scale-Space Theories in Computer Vision, 1999.

F. Meyer, C. Vachier, A. Oliveras, and P. Salembier, Morphological tools for segmentation: Connected filters and watersheds, Annales des télécommunications, vol.52, pp.367-379, 1997.

R. Mottaghi, X. Chen, X. Liu, N. Cho, S. Lee et al., The role of context for object detection and semantic segmentation in the wild, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.

M. Nagao, T. Matsuyama, and Y. Ikeda, Region extraction and shape analysis in aerial photographs, CGIP, vol.10, issue.3, pp.195-223, 1979.

L. Najman, On the equivalence between hierarchical segmentations and ultrametric watersheds, JMIV, vol.40, issue.3, pp.231-247, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00419373

L. Najman, J. Cousty, and B. Perret, Playing with kruskal: algorithms for morphological trees in edge-weighted graphs, ISMM, pp.135-146, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00798621

L. Najman and M. Schmitt, Geodesic saliency of watershed contours and hierarchical segmentation, IEEE PAMI, vol.18, issue.12, pp.1163-1173, 1996.
URL : https://hal.archives-ouvertes.fr/hal-00622128

O. Okubadejo, E. Andò, L. Bonnaud, G. C. Viggiani, and M. Dalla-mura, Contact based hierarchical segmentation for granular materials, ISMM, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02097130

G. K. Ouzounis and P. Soille, The alpha-tree algorithm. Publications Office of the European Union, 2012.

T. Pavlidis, Structural pattern recognition, vol.2, 1977.

B. Perret, J. Cousty, S. J. Guimaraes, and D. S. Maia, Evaluation of hierarchical watersheds, IEEE TIP, vol.27, issue.4, pp.1676-1688, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01430865

B. Perret, J. Cousty, J. C. Ura, and S. J. Guimarães, Evaluation of morphological hierarchies for supervised segmentation, ISMM, pp.39-50, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01142072

J. Pont-tuset, P. Arbeláez, J. T. Barron, F. Marques, and J. Malik, Multiscale combinatorial grouping for image segmentation and object proposal generation, IEEE PAMI, vol.39, issue.1, pp.128-140, 2017.

J. Pont-tuset and F. Marques, Supervised assessment of segmentation hierarchies, Computer Vision-ECCV 2012, pp.814-827, 2012.

J. Pont-tuset and F. Marques, Upper-bound assessment of the spatial accuracy of hierarchical region-based image representations, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.865-868, 2012.

J. Pont-tuset and F. Marques, Measures and meta-measures for the supervised evaluation of image segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.2131-2138, 2013.

J. Pont-tuset and F. Marques, Supervised evaluation of image segmentation and object proposal techniques, IEEE PAMI, vol.38, issue.7, pp.1465-1478, 2016.

J. F. Randrianasoa, C. Kurtz, E. Desjardin, and N. Passat, Binary partition tree construction from multiple features for image segmentation, Pattern Recognition, vol.84, pp.237-250, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01248042

P. Salembier and L. Garrido, Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval, IEEE transactions on Image Processing, vol.9, issue.4, pp.561-576, 2000.

P. Salembier, A. Oliveras, and L. Garrido, Antiextensive connected operators for image and sequence processing, TIP, vol.7, issue.4, pp.555-570, 1998.

P. Salembier and M. H. Wilkinson, Connected operators, IEEE Signal Processing Magazine, vol.26, issue.6, pp.136-157, 2009.

J. Shi and J. Malik, Normalized cuts and image segmentation. Departmental Papers (CIS), p.107, 2000.

A. G. Silva, R. De-alencar, and . Lotufo, New extinction values from efficient construction and analysis of extended attribute component tree, SIBGRAPI, pp.204-211, 2008.

P. Soille, Constrained connectivity for hierarchical image partitioning and simplification, IEEE transactions on pattern analysis and machine intelligence, vol.30, pp.1132-1145, 2008.

R. Souza, L. Rittner, R. Machado, and R. Lotufo, A comparison between extinction filters and attribute filters, International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing, pp.63-74, 2015.

M. Stoer and F. Wagner, A simple min-cut algorithm, Journal of the ACM (JACM), vol.44, issue.4, pp.585-591, 1997.

C. Straehle, S. Peter, U. Köthe, and F. A. Hamprecht, K-smallest spanning tree segmentations, German Conference on Pattern Recognition, pp.375-384

. Springer, , 2013.

C. Tomasi and R. Manduchi, Bilateral filtering for gray and color images, Iccv, vol.98, 1998.

R. Trias-sanz, G. Stamon, and J. Louchet, Using colour, texture, and hierarchial segmentation for high-resolution remote sensing. ISPRS Journal of Photogrammetry and remote sensing, vol.63, pp.156-168, 2008.

C. Vachier and F. Meyer, Extinction value: a new measurement of persistence, IEEE Workshop on nonlinear signal and image processing, vol.1, pp.254-257, 1995.

S. Valero, P. Salembier, and J. Chanussot, New hyperspectral data representation using binary partition tree, 2010 IEEE International Geoscience and Remote Sensing Symposium, pp.80-83, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00578960

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.

L. Vincent and P. Soille, Watersheds in digital spaces: an efficient algorithm based on immersion simulations, IEEE Transactions on Pattern Analysis & Machine Intelligence, issue.6, pp.583-598, 1991.

S. Wang and J. M. Siskind, Image segmentation with minimum mean cut, ICCV, vol.1, pp.517-524, 2001.

J. H. Ward, Hierarchical grouping to optimize an objective function, Journal of the American statistical association, vol.58, issue.301, pp.236-244, 1963.

Y. Xu, E. Carlinet, T. Géraud, and L. Najman, Hierarchical segmentation using tree-based shape spaces, IEEE PAMI, vol.39, issue.3, pp.457-469, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01301966

Y. Xu, T. Géraud, and L. Najman, Context-based energy estimator: Application to object segmentation on the tree of shapes, 19th IEEE International Conference on Image Processing, pp.1577-1580, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00762289

Y. Xu, T. Géraud, and L. Najman, Morphological filtering in shape spaces: Applications using tree-based image representations, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), pp.485-488, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00714847

C. T. Zahn, Graph theoretical methods for detecting and describing gestalt clusters, IEEE Trans. Comput, vol.20, p.68, 1970.

V. and E. ,

, Appendix: proofs of theorems and properties XXI Lemma 69. Let ? be an altitude ordering for w and let H be a hierarchy on V such that ?(H) is one-side increasing for ?

, As ?(H) is one-side increasing for ?, by the condition 1 of Definition 18, we can affirm that ? = 0 + 1 + · · · + n ? 1. In order to prove that (V, E ? ) is a MST of (G, ?(H)), we will prove that, for any MST G of (G, ?(H)), the sum of the weight of the edges in G is greater than or equal to ?. Let G be a MST of (G, ?(H)). As G is a MST of (G, ?(H)), by the condition 1 of Lemma 83, we have that G and G have the same quasi-flat zones hierarchy, map ?(H): ? = e?E? ?(H)(e)

.. .. {1, ;. .. , and ,. , Since the partitions P i and P i?1 are distinct, then there exists a region in P i which is not in P i?1 . Therefore, there is a region X of P i which is composed of a several regions {R 1, n ? 1} is a subset of the range of ?(H)

}. Hence, the lowest j such that x and y belong to the same region of P j is i. Thus, there exists an edge u = {x

, by Definition 21, there is a hierarchical watershed H w of (G, w) such that H is a flattening of H w . By Theorem 19, there is an altitude ordering ? for w such that ?(H w ) is one-side increasing for ?. Let ? be the altitude ordering for w such that ?(H w ) is one-side increasing for ?, By Lemma, vol.84

, Then, by Lemma 71, there is an edge u = {x, y} such that u is in E \ E(G ) and such that ?(H)(u) is different from the greatest weight among the edges in the path ? from x to y in (G , ?(H)). Let v be an edge of greatest weight in ?. As H is equal to QFZ(G, ?(H)), we may affirm that ?(H)(u) is lower than ?(H)(v) because, otherwise, the vertices x and y would be connected in the ?-level set of (G, ?(H)) for a ? lower than ?(H)(u), which contradicts the fact that ?(H) is a saliency map. Hence, we have ?(H)(u) < ?(H)(v). Then, by Lemma 73, as H is a flattening of H w

, As H w is one-side increasing for ?, by the second condition of Definition 18, for any watershed-cut edge u = {x, y} for ?, we have ?(H w )(u) = 0. Then, for any partition P of H w , x and y belong to the same region of P. Therefore, as any partition of H is a partition of H w , we can say that, for any partition P of H, x and y belong to the same region of P. Hence, the lowest ? such

, We will now prove the third condition for H to be a flattened hierarchical watershed of (G, w), XXIV Appendix: proofs of theorems and properties

?. E-?-;-|-r-v and . R}, Let u be an edge in E ? and let R be the child of R u such that ?(H w )(u) ? ?{?(H w )(v) | R v ? R}, there exists a child R of R u such that ?(H w )(u) ? ?{?(H w )(v)

, Lemma 75. Let H be a hierarchy on V and let ? be an altitude ordering for w such that: 1. (V, E ? ) is a MST of

, Then H is a flattened hierarchical watershed of (G, w)

, In order to prove Lemma 75, we first state two auxiliary lemmas. From Property 10 of [27], we can deduce the following lemma linking binary partition hierarchies and MSTs

, Let B be a binary partition hierarchy of (G, w), Lemma, vol.76

, By Property 12 of [27] linking hierarchical watersheds and hierarchies induced by an altitude ordering and a sequence of minima, and by Lemma 83, we infer the following lemma

, Let G be a MST of (G, w) and let H be a hierarchical watershed of (G , w), Lemma

, Then H is also a hierarchical watershed of (G, w)

, ?(H)); and 2. for edge u in E ? , if u is not a watershed-cut edge for ?, then ?(H)(u) = 0; and 1. By the definition of f , as there are n?1 watershed-cut edges for ?, we can say that, for any i in {1, of Lemma 75. Let H be a hierarchy on V such that there is an altitude ordering ? for w such that: 1. (V, E ? ) is a MST of

, by the definition of f , f (u) is non-zero if and only if u is not a watershed-cut edge for ?, so the statement 2 of Definition 18 holds true for f . XXVI Appendix: proofs of theorems and properties

, no minimum of w included in X. Hence, none of the building edges of the descendants of X is a watershed-cut edge for ?. By the definition of f , we have f (u) = 0 and, for any edge v such that R v ? X, we have f (v) = 0. Hence, f (u) ? ?{f (v) such that R v is included in X}. Otherwise, let us assume that u is a watershed-cut edge for ?. Then there is at least one minimum of w included in each child of R u . By the hypothesis 3, there is a child X of R u such that ?(H)(u) ? ?{?(H)(v) such that R v is included in X}

, Therefore, f (u) ? ?{f (v) such that R v is included in X}. Then, the third condition of Definition 18 holds true for f

, QFZ(G , f ) is a hierarchical watershed of (G , w) (resp. (G, w)). Now, we only need to prove that any partition of H is a partition of QFZ(G , f ), Hence, f is one-side increasing for ? and, as stated previously

.. .. |-u-?-e-?-}-=-{0 and }. , Let G ?,?(H) be the ?-level set of (G , ?(H)). Let ? be the greatest value in {f (u) | u ? E(G ?,?(H) )}. We will prove that the ?-level set of (G , f ) is equal to the ?-level set of (G , ?(H)), }: {?(H)(u)

, ?(H)). Then, ?(H)(u) > ? and, for any edge v in the ?-level set of (G , ?(H)), we have ?(H)(u) > ?(H)(v)

, if v is a watershed-cut edge for ?, then v ? 2 u and f (u) > f (v). Otherwise, if v is not a watershed-cut edge for ?, by the definition of f , we have f (v) = 0 and f (u) > f (v). Thus, for any edge v in the ?-level set of (G , ?(H))

, Therefore, we can conclude that the ?-level set of (G , f ) is equal to the ?-level set 1. { (R) | R is a region of B ? } = {0

, any two distinct minima M 1 and M 2 of w, we have (M 1 ) = (M 2 ); and 3. for any region R of B ? , we have that (R) is equal to ?{ (M ) such that M is a minimum of w

, We will prove that the statements 1, 2 and 3 hold true for . To prove that the statement 1 holds true, we will first prove that { (R) | R is a region of B ? } ? {0, Let be an extinction map for ?. Then, by the definition of extinction maps, there is a sequence S = (M 1

.. .. {-(r)-|-r-is-a-region-of-b-?-}-=-{0 and . N},

, any two distinct minima M 1 and M 2 of w, we have (M 1 ) = (M 2 ); and 3. for any region R of B ? , we have that (R) is equal to ?{ (M ) such that M is a minimum of w

. Proof, M n ) of minima of w such that, for any region R of B ? , the value (R) is the extinction value of R for

.. Let-s-=-(m-1, M n ) be a sequence of minima of w ordered in non-decreasing order for , i.e., for any two distinct minima M i and M j , with i, j in {1, n}, if (M i ) < (M j ) then i < j. 1. (V, E ? ) is a MST of

, Let B be a binary partition hierarchy of (G, w). Then, any minimum of w is a region of B, Lemma, vol.80

, M n ) be a sequence of minima of w and let ? be the persistence map for (S, ?). The range of ? is {0, Lemma 81. Let ? be an altitude ordering on the edges of G for w, let S = (M 1, p.1

S. and ?. , We will prove that (1) for any building edge u for ?, ?(u) is in {0, Proof. Let be the extinction map for

V. and E. , is a MST of (G, ?(H)); and 2

V. and E. , By Lemma 83 (statement 1), as G is a MST of (G, ?(H)), we have that G and G have the same quasi-flat zones hierarchies

, By the definition of persistence values, we can affirm that

?. and S. , Hence, by Lemma 83, G and G have the same quasi-flat zones hierarchies (for ?(H )): QFZ(G , ?(H )) = QFZ(G, ?(H )), G is a MST of, vol.84

.. |-u-?-e-?-}-=-{0, n ? 1} and, by Definition 18 (statement 2), only the weight of the watershed-cut edges for ? are strictly greater than zero. Then, {f (e) | e is a watershed ? cut edge f or ?} = {1, n ? 1}. Hence, for any i in {1, vol.18

, Let ? be an altitude ordering for w, let f be a map from E into R such that f is one-side increasing for ?, and let ? be the approximated extinction map for (f, ?). The range of ? is {0, Lemma, vol.87

, We will prove that: (1) for any i in {0, . . . , n}, there is a region R of B ? such that ?(R) = i; and (2) for any region R of B ? , we have ?(R) in {0

, We start by proving that there is a region R of B ? such that ?(R) = n. Let R be the set V of vertices of G. Then, by Definition 27 (statement 1), we have ?(R) = k + 1, We first prove statement

, Otherwise, let v be the building edge of the parent of R. By Definition 27, the value ? f (R) is either f (v) or ?(parent(R)). Hence, either ? f (R) is equal to f (v) for a building edge v for ?, or ? f (R) is equal to ?(V ) = n. It is enough to prove that n and f (v) are in {0, . . . , n}. As f is one-side increasing for ?, Let R be a region of B ? . If R = V , then ?(R) = n, as established in the proof of statement

, Let ? be an altitude ordering for w and let f be a map from E into R such that f is one-side increasing for ?. Let ? be the approximated extinction map for (f, ?), For any two minima M 1 and M 2 of w, if ?(M 1 ) = ?(M 2 )

, Y )) = f (u X ); and 3. there is a minimum of w included in Y

, We consider two cases: (1) sibling(Z) is a leaf-region of B ? ; and (2) sibling(Z) is a non-leaf region of B ? . (1) If sibling(Z) is a leaf-region of B ? , then, by Definition 26, Z is a dominant region for (f, ?) and sibling(Z) is not a dominant region for (f, ?). Hence, by Definition, Proof. Let X be a region such that there is a minimum M of w such that M ? X. Then, there is a child Z of X such that there is a minimum M such that M ? Z. Let Z be a child X such that there is a minimum M such that M ? Z, vol.27

, Let us now assume that sibling(Z) is a non-leaf region of B ? . Since X is not a minimum of w and since there is a minimum of w included in Z, we can conclude that there is a minimum of w included in sibling(Z) as well. Let be the non-leaf ordering for (f, ?)

Z. Then, by the definition of dominant regions (Definition 26), we have that either Z or sibling(Z) is a dominant region for (f, ?)

, Since both Z and sibling(Z) include at least one minimum of w, we may say that there is a child Y of X for which the hypothesis 1, 2 and 3 hold true, Otherwise, if sibling(Z) is a dominant region for (f, ?)

, Let ? be an altitude ordering for w and let f be a map from E into R such that f is one-side increasing for ?. Let ? be the approximated extinction map for (f, ?), Lemma 90

M. ?-r-u, If Y 1 is a minimum of w, then the property holds true. Otherwise, if Y 1 is not a minimum of w, it means that there is a minimum M of w such that M ? Y 1 . By Lemma 89, there is a child Y 2 of Y 1 such that ?(Y 2 ) = ?(Y 1 ) = f (u) and such that there is a minimum of w included in Y 2 . Again, if Y 2 is a minimum of w, then the property holds true. Otherwise, we can apply this same reasoning indefinitely. We can define a sequence

, Let ? be an altitude ordering for w and let f be a map from E into R such that f is one-side increasing for ?, Lemma 91

, Otherwise, by Lemma 89, there in Y 2 . Again, if Y 2 is a minimum of w, then the property holds true. Otherwise, we can apply this same reasoning indefinitely, X is a minimum of w, then it is trivial

, Proof of Lemma 88, order to prove that (1) for any two minima M 1 and M 2 of w, if ?(M 1 ) = ?(M 2 ), then M 1 = M 2 , we will prove that

, Appendix: proofs of theorems and properties XXXVII

.. |-r-v-?-v-}-=-{0, By Lemma 86, for any i in {1, . . . , n ? 1}, there is a watershed-cut edge such that f (u) = i. Then, for any i in {1, As w has n minima, it suffices to prove that, for any i in {1, . . . , n}, there is a minimum M of w such that ?(M ) = i. By Lemma 90, for any watershed-cut edge u for B ? , there is a minimum M such that ?(M ) = f (u)

, Since w has n minima, it implies that the values ?(M 1 ) and ?(M 2 ) are distinct for any pair (M 1 , M 2 ) of distinct minima of w. Hence, for any two minima M 1 and M 2 of w, if ?(M 1 ) = ?(M 2 ), then M 1 = M 2

, Let ? be an altitude ordering for w and let f be a map from E into R such that f is one-side increasing for ?. Let ? be the approximated extinction map for (f, ?), Lemma 92

, Let ? be an altitude ordering for w and let f be a map from E into R such that f is one-side increasing for ?, Lemma 93

, Let X be a region of B ? . Then ?(X) is greater than or equal to the supremum descendant value of X (for

X. If, X) = ?(V ) = k + 1, where k is the supremum descendant value of X for (f, ?) (first case of Definition 27). Then, ?(X) is clearly than the supremum descendant value of X

X. If, not a dominant region for (f, ?), then ?(X) = f (u) (third case of Definition 27)

.. |-v-?-e-?-}-=-{0, then there is no descendant of X whose building edge is a watershed-cut edge for ?. Hence, for any edge v such that R v ? X, u is not a watershed-cut edge for ? and, since f is one-side increasing for ?, we have f (v) = 0 Definition 18 (statement 2). Therefore, the supremum descendant value of X is zero. By Definition 18 (statement 1), we have {f (v), XXXVIII Appendix: proofs of theorems and properties (a) If there is no minimum M of w such that M ? X

, If X sibling(X), then, by the definition of non-leaf ordering

. |-r-v-?-y-} and . Hence, Since f is one-side increasing for ?, by the statement 3 of Definition 18, there is a child Y of parent(X) such that f (u) ? ?{f (v), Thus, we have (X) ? (sibling(X))

?. , Then, we have f (u) ? (X) or f (u) ? (sibling(X)). In the case where f (u) ? (sibling(X)), this also implies that f (u) ? (X) because (X) ? (sibling(X)). Therefore, ?(X)

, In the base step, we consider that parent(X) is V . In the inductive step, we show that, if the property holds true for parent(X), then it also holds true for X. Please note that, if parent(X) is not a dominant region for (f, ?), the property holds for parent(X) as proven in the previous case. (a) Base step: if parent(X) is V , then ?(X) = ?(V ) = (V ) + 1 (first case of Definition 27). We can see that (V ) ? (X) because

, Since ?(X) = ?(parent(X)), we have ?(X) ? (parent(X)). We can affirm that, for any edge v in E ? such that R v ? X, we also have R v ? parent(X). Hence, (parent(X)) ? (X). Therefore, ?(X)

, Appendix: proofs of theorems and properties XXXIX Proof of Lemma 92. We will prove that, for any region X of B ?

, The edge u is not a watershed-cut edge for ? because the child X of R u does not include any minimum of w. Hence, since f is one-side increasing for ?, by the statement 2 of Definition 18, X, then X is not a dominant region for

, X is a minimum of w, then ?(X) = ?{? f (M ) such that M is a minimum of w included in X} = ?{? f (X)}

|. X}, To prove that ?(X) ? ?{?(Y ) | Y ? X}, it is enough to demonstrate that, for any region Z of B ? , we have ?(Z) ? ?{?(Y ) | Y is a child of Z}. Let Z be a region of B ? . If Z is a leaf region of B ? , then ?(Z) ? ?{?(Y ) | Y is a child of Z} = ?{} = 0 because, by Lemma 87, ?(Z) is in {0, . . . , n}. Let us now assume that Z is not a leaf region of B ? and let Y be a child of Z. If Y is a dominant region for (f, ?), then ?(Y ) = ?(Z) and, consequently, ?(Z) ? ?(Y ). Otherwise, if Y is not a dominant region for (f, ?), then ?(Y ) = f (v), where v is the building edge of, Z. By Lemma, vol.93

, We can now prove that ?(X) = ?{? f (M ) such that M is a minimum of w included in X} in the case where X is not a minimum of w. By Lemma 91, there is a minimum M of w such that M ? X and such that ?(M ) = ?(X). Let M be the minimum of w such that ?(M ) = ?(X). Since ?(X) ? ?{?(Y ) | Y ? X}, we can say that ?(X) = ?{? f (M )

, Let ? be an altitude ordering for w and let f be a map from E into R such that the approximated extinction map for (f, ?) is an extinction map for ?. Then, f is one-side increasing for ?. XL Appendix: proofs of theorems and properties = {0, Lemma 94, p.1

.. .. {0, ? 1} and let R be a region of B ? such that ?(R) = i. If R is not a dominant region for (f, ?), then ?(R) = f (u), where u is the building edge of the parent of R and, then, we can affirm that there exists an edge in E ? whose weight for f is i. Otherwise, if R is a dominant region for (f, ?), then ?(R) = ?(parent(R)). If parent(R) is not a dominant region for (f, ?), then ?(parent(R)) = ?(v), where v is the building edge of the parent of parent(R) and we have our property. Otherwise, if parent(R) is a dominant region for (f, ?), then ?(parent(R)) = ?(parent(parent(R))). We can see that, for any leaf region R of B ? , we have ?(R) = ?{?(M ) such that M is a minimum of w included in R} = 0

, Let ? be an altitude ordering for w and let f be a map from E into R. Let ? be the approximated extinction map for (f, ?) such that ? is an extinction map. Then

, at least one minimum of w included in each child of R u . Hence, by Property 23, the value of each child of R u in ? is greater than zero. As there is at most one child of R u that is a dominant region for (f, ?), then there is a child of R u that is not a dominant region for (f, ?)

, Since ? is an extinction map and since there is no minimum of w

, Let ? be an altitude ordering for w and let f be a map from E into R. Let be a map from each region of B ? into its supremum descendant value (for

, Let ? be the approximated extinction map for (f, ?) such that ? is an extinction map. Then

, Let ? be the approximated extinction map for (f, ?) such that ? is an extinction map. Then, for any region R of B ? , ?(R) ? ?{?(X) | X ? R}. dominant region for (f, ?), Property 98. Let ? be an altitude ordering for w and let f be a map from E into R

}. X-?-r-u and . Then, At most one of the children of R u is a dominant region for (f, ?), by Property 99, we have ?(R u ) ? {f (v) | v is the building edge of a region X ? R}

, Let f be a map from E into R, let ? be a lexicographic ordering for (w, f ), and let ? be the approximated extinction map for (f, ?), vol.28

, Thus, by Lemma 85, ? is an extinction map for ?. Backward implication: Let ? be an extinction map for ?. Then, by Property 94, f is one-side increasing for ?. Hence, Theorem 20, f is one side increasing for the lexicographic ordering ? for (w, f )

, If f is the saliency map of a hierarchical watershed of (G, w), then f is the saliency map of a hierarchical watershed of (G, w) for S. To prove Property 30

, Let f be a map from E into R and let ? be an altitude ordering for w. Let ? be the approximated extinction map for (f, ?), and let S be the estimated sequence of minima for (f, ?)

, Therefore, S is a sequence of minima ordered according to their extinction values in ?. Therefore, S corresponds to the estimated sequence of minima for, Proof. Let ? be an extinction map for ?. Then, there exists a sequence S = (M 1

, Proof of Property 30. Let f be the saliency map of a hierarchical watershed of (G, w)

, Hence, for any u in E ? , we have f (u) = min{?(R u ), f (u)}. By the definition of approximated extinction maps (Definition 27), we can conclude that, for any child X of R(u), we have either ?(X) = ?(R u ) or ?(X) = f (u). Hence, f (u) = min{?(R) | R is a child of R u }. By Property 100, ? is the extinction map for S and ?. Hence, f maps any building edge u for ? into its persistence value for, Then, by Theorem 28, ? is an extinction map. By Property 99, for any u in E ? , we have ?(R u ) ? f (u)

, The following statements hold true: 1. The hierarchy H is a hierarchical watershed of (G, w) if and only if the watersheding ?(f ) of f (for ?) is equal to f

, The watersheding ?(f ) of f is the saliency map of a hierarchical watershed of (G, w)

, The watersheding ?(?(f )) of ?(f ) is equal to ?(f )

, To prove Theorem 33, we first present the following lemma

, We will prove that f is also the saliency map of the hierarchy induced by (S, ?). By Property 30, the map f is the saliency map of a hierarchical watershed of (G, w) for S. Therefore, by Lemma 8.2.5, the map f is the saliency map of the hierarchy induced by (S, ?). Thus, the watersheding ?(f ) of f is equal to f . Backward implication: Let ?(f ) be equal to f . Let S be the estimated sequence of minima for (f, ?). By Theorem 32, the map ?(f ) is the saliency map of a hierarchical watershed of (G, w) for S. Hence, S be a sequence of minima of w, and let f be the saliency map of a hierarchical watershed of (G, w) for S. Let ? be a lexicographic ordering for

, Statement 2: Let S be the estimated sequence of minima for f and ?. By Theorem 32, the watersheding ?(f ) of f is the saliency map of a hierarchical watershed of (G, w) for S. Hence, ?(f ) is the saliency map of a hierarchical

, By the statement 2, the watersheding ?(f ) of f is the saliency map of a hierarchical watershed of (G, w). Hence, by the first statement, the watersheding ?(?(f )) of (?(f ), vol.3

, Let (G, w) be a weighted graph and let ? be an altitude ordering for w. map for (S, ?), Lemma, vol.101

, By Property 6, since f is the saliency map of the hierarchical watershed of (G, w) for S, then f is the saliency map of the hierarchy induced by S and by an altitude ordering for w. Since ? is the unique altitude ordering for w, then f is the saliency map of the hierarchy

, Therefore, the value f (u) is min{ (X), (Y )}, where X and Y are the children of R u

, Let (G, w) be a tree with a unique altitude ordering and let ? be the altitude ordering for w. Let S be a sequence of minima of w and let be the extinction map for (S, ?), Lemma, vol.103

, Let f be the saliency map of the hierarchical watershed of (G, w) for S. Let u be a watershed-cut edge for ? and let M be a minimum of w. If M is not the minimum of greatest extinction value among the minima, Lemma, vol.104

, By Lemma 103, the weight of each watershed-cut edge v such that R v ? R u is the extinction value of a minimum included in R u . Moreover, by Lemma 19, f is one-side increasing for ? and, consequently, the watershed-cut edges have pairwise distinct edge weights in f . Therefore, for ? 1 minima included in R u , there is a watershed-cut edge v such that R v ? R u and such that f (u) is the extinction value of this minimum. Hence, there is only one minimum M included in R u such that there is no watershed-cut edge v such that R v ? R u and f (u) = (M )

, Let f be the saliency map of the hierarchical watershed of (G, w) for S. Let u be a watershed-cut edge for ?. Let M be the minimum of maximum extinction value among the minima included in R u . Then, for any watershed, Lemma 105. Let (G, w) be a tree with a unique altitude ordering and let ? be the altitude ordering for w

. R-v-?-r-u, By Lemma 103, the value f (v) is the extinction value of a minimum included in R u . By Lemma 104, f (u) is different from the extinction value of M . As the minima of w have pairwise distinct extinction values and since the extinction value of M is maximal among the minima

, Let M x be the minimum included in X such that (X) = (M x ) and let M y be the minimum included in Y such that (Y ) = (Y x ). The region R u is a maximal region of B ? for f if and only if: 1. either M x is the only minimum included in X; or 2, Lemma 106. Let (G, w) be a weighted graph and let ? be the unique altitude ordering for w

, We first prove the forward implication. We consider the conditions 1 and 2 separately

. |-r-v-?-y-}, a maximal region of B ? for f , we will first prove that f (u) > {f (v) | R v ? X} and, then, we will prove that f (u) > {f (v) | R v ? Y }. Let r be the building edge of X. By Definition 2, r is not a watershed-cut edge for ?. Hence, since f is one-side increasing for ? by Lemma 19, we can say that f (r) = 0 by the second statement of Definition 18. Therefore, by the third statement of Definition 18, for any edge r such that R r ? R v , we have f (r ) = 0. Since u is a watershed-cut edge for ?, we have f (u) > 0 by the second statement of Definition 18, By Lemma, vol.102

. R-v-?-r-u-=-?, In this case, for any region Z such that Z ? R v , we have (Z) = (Z) because the position of the minima included in R u are the same in the sequences S and S . Since f (v) is defined by the extinction value of the children of R v

. R-v-=-r-u, ) is min{ (X), (Y )}. Let M x be the minimum of second greatest extinction value among the minima included in X. By Property 106, since (M x ) > (M y ), we have (M x ) < (M y ). Therefore, we can say that M x (resp. M y ) is still the minimum of w of greatest extinction value for among the minima included in X (resp. Y ). Hence, we have that (X) = (M x ) = (Y ) and that (Y ) = (M y ) = (X). Hence, f (u) = min{ (X)

. R-u-?-r-v, The only minima that had their extinction values changed in with respect to were two minima M x and M y included in R u . Hence, the greatest extinction value among the minima included in R u is still the same even though the minimum carrying this extinction value has changed. Therefore, the extinction value of both children of R v has not been

. R-v-?-r-u, We will consider the two following cases: (a) R v ? X; and (b) R v ? Y . Appendix: proofs of theorems and properties XLIX

R. and ?. , Otherwise, let us assume that M x is included in R v . If M x = R v , then v is not a watershed-cut edge for ? and we have f (u) = f (u) = 0 by the second statement of Definition 18. Otherwise, let R be the child of R v that includes M x . By our assumption, M x is the minimum of w of greatest extinction value among the minima included in X. Therefore, (R ) = (M x ). Moreover, f (v), being min{ (R ), (sibling(R ))}, is equal to (sibling(R )). Let M x be the minimum of w of second greatest extinction value among the minima included in X, If M x is not included in R v , we can conclude that the extinction value for of all regions included in R v have not been changed with respect to , which implies that f (u) = f (u), vol.106

R. and ?. , By our assumption, M y is the minimum of w of greatest extinction value among the minima included in Y . Hence, f (v), being min{ (R ), (sibling(R ))}, is equal to (sibling(R )). By our hypothesis, If M y is not included in R v , we can conclude that the extinction value for of all regions included in R v have not been changed with respect to , which implies that f (u) = f (u)

, Hence, there is no minimum included in R v whose extinction value for S is equal to (M x ). Therefore, by Lemma 105, f (v) < (M x ) and, consequently, f (v) = f (v), which contradicts our assumption. Hence, M x is the minimum of greatest extinction value among the minima included in X. The same argument can be used to L Appendix: proofs of theorems and properties prove that M y is the minimum of greatest extinction value among the minima, We now prove the backward implication. Let f and f be equal. Using Lemma 106, we will prove by contradiction that R u is a maximal region of B ? for f and that M x (resp. M y ) is the minimum of w of greatest extinction value among the minima included in X (resp. Y )

, Let M x be the minimum included in X with the second greatest extinction value among all minima included in X. As R u is not a maximal region, Lemma 106, we have that (M x ) ? (Y ). We know that (M x ) = (M y ) = (Y ) and that (M y ) = (M x ) = (X). Since (M x ) ? (Y ) and since (M x ) = (Y ), then M x became the minimum of greatest extinction value for S among the minima included in X. However, (M x ) is still less than (M y ) = (M x ). In the end, we will have (X) = (M x ) and (Y ) = (M x ). Since f (u) = min{ (X), (Y )}, we have that f (u) = (M x ). By our assumption that (M x ) > (M y )

, | w) is equal to |S H | |Mw| . Hence, we need to prove that |S H | is equal to 2 m . To this end, we will prove that, given any sequence S in S w (H), we can obtain another sequence in S H only by, for each maximal region R of B ? for f , swapping the order of two minima included in R. By Lemma 108, for each maximal region R of B ? for f , there is only one pair of minima of (G, w) and let ? be an altitude ordering for (G, w) such that both ?(H 1 ) and ?(H 2 ) are one-side increasing for ?, Proof of Property 37. Let f be the saliency map of H. By Property, vol.35

, Appendix: proofs of theorems and properties LI

, ?(H 2 )). We will prove that the hierarchy QFZ(G, f 3 ) is a flattened hierarchical watershed of (G, w), Let H 1 and H 2 be two hierarchical watersheds of (G, w) and let ? be an altitude ordering for w such that both ?(H 1 ) and ?(H 2 ) are one-side increasing for ?. Let f 3 denote the map (?(H 1 )

, The following Lemmas 109, 112 and 113 prove respectively that the conditions 1, 2 and 3 for QFZ

, Let f 1 and f 2 be two maps from E into R and let G be a subgraph of G such that G is a MST of both (G, f 1 ) and (G, f 2 ). Then G is also a MST of (G

, the context of graphs and we state two well-known properties of spanning trees in Lemmas 110 and 111. Let x and y be two vertices in V and let ? = (x 0

(. Then and E. {u},

G. , As G is a spanning tree, by Lemma 110, the graph (V, E(G ) ? {u}) contains a cycle ? which includes the edge u. Since G is a MST for (G, f 1 ) and for (G, f 2 ), by the forward implication of Lemma 111, Lemma 111. Let G be a spanning tree of a weighted graph

, (v), f 2 (v)) ? min(f 1 (u), f 2 (u)) and, consequently, f 3 (v) ? f 3 (u). Hence, for any edge v in ?, the cycle ?, we have min

, Thus, by the backward implication of Lemma 111, G is a MST of

, The following lemma proves that the condition 2 for QFZ(G, f 3 ) to be a flattened hierarchical watershed hold true. LII Appendix: proofs of theorems and properties

, The following lemma proves that the condition 3 for QFZ(G, f 3 ) to be a flattened hierarchical watershed holds true

, Let f 1 and f 2 be two maps from E into R and let B be a binary partition hierarchy of (G, w) such that f 1 and f 2 are one-side increasing for ?

, we have that, for any building edge u of B, f 1 (u) ? ?{f 1 (v) | R v ? X} (resp. f 2 (u) ? ?{f 2 (v) | R v ? X}) for a child X of R u . We need to prove that, Proof. Since f 1 (resp. f 2 ) is one-side increasing for ?, vol.18

, Let X and Y be the children of R u . If f 1 (u) ? ?{f 1 (v) | R v ? X} (resp. f 1 (u) ? ?{f 1 (v) | R v ? Y }), we can affirm that f 3 (u) ? ?{f 1 (v) | R v ? X} (resp. f 3 (u) ? ?{f 1 (v) | R v ? Y }) as well, Since f 3 (e) = min(f 1 (e), f 2 (e))

, By Lemma 69, we can affirm that (V, E ? ) is a MST of both (G, ?(H 1 )) and (G, ?(H 2 )). Let f 3 denote the map (?(H 1 ), ?(H 2 )). By Lemma 109, (V, E ? ) is a MST of (G, f 3 ) as well, vol.46

, C(b, a); and 2. if min(a, b) < min(c, d), then C(a, b) < C(c, d); and 3. if min(a, b) = min(c, d) and max(a, b) < max(c, d)

, Let H 1 and H 2 be two hierarchical watersheds of (G, w) and let ? be an altitude ordering for w such that both ?(H 1 ) and ?(H 2 ) are one-side increasing for ?. Let C be a positive function from R 2 into R such that

, C(0, 0) = 0; and

, C(b, a); and 3. if min(a, b) = min(c, d) and max(a, b) < max(c, d) then C(a, b) ? C(c, d)

, By Property 22, we need to prove that there exists a binary partition hierarchy B of (G, w) such that the following statements hold true: 1. (V, E(B )) is a MST of (G, f 3 ); and 2

, Let C be a function from R 2 into R such that, for any two real values x and y, we have C(x, y) = C(y, x)

, As min(a, b) = min(c, d) and max(a, b) = max(c, d), then either we have (i) a = c and b = d which implies that C(a, b) = C(c, d); or (ii) c = b and d = a which implies that C(c, d) = C(b, a), which, by our hypothesis on C

, The following three lemmas prove that the conditions 1, 2 and 3 for QFZ(G, f 3 ) to be a flattened hierarchical watershed of (G, w) hold true

, Lemma 115. Let C be a positive function such that

, C(0, 0) = 0; and

, C(b, a); and 3. if min(a, b) = min(c, d) and max(a, b) < max(c, d) then C(a, b) ? C(c, d)

, Let f 1 and f 2 be the saliency maps of two hierarchies on V and let G be a subgraph of G such that G is a MST of both (G, f 1 ) and (G, f 2 ). Then G is also a MST of

, Let f 3 denote the map C(f 1 , f 2 )

G. As, Lemma 111, for any edge v in the cycle ?, we have f 1 (v) ? f 1 (u) and f 2 (v) ? f 2 (u). Therefore, for any edge v in the cycle ?, we have min(f 1 (v), f 2 (v)) ? min(f 1 (u), f 2 (u)) and max(f 1 (v), f 2 (v)) ? max

&. , Then, we should consider the three following cases: 1. If min(f 1 (v), f 2 (v)) < min

, (v)) = min(f 1 (u), f 2 (u)) and max

, C(0, 0) = 0; and

, C(b, a); and 3. if min(a, b) = min(c, d) and max(a, b) < max(c, d) then C(a, b) ? C(c, d)

, Let f 1 and f 2 be the saliency maps of two hierarchies on V and let B be a binary partition hierarchy of (G, w) such that both f 1 and f 2 are one-side increasing for ?

, Lemma 117. Let C be a positive function such that

, C(0, 0) = 0; and

, C(b, a); and 3. if min(a, b) = min(c, d) and max(a, b) < max(c, d) then C(a, b) ? C(c, d)

, Let f 1 and f 2 be the saliency maps of two hierarchies on V and let B be a binary partition hierarchy of (G, w) such that both f 1 and f 2 are one-side increasing for ?. Let f 3 denote the map C(f 1 , f 2 ). Then, for any

. |-r-v-?-x})-for-a-child-x-of-r-u, We need to prove that, for any building edge u of B, there is a child X of R u such that f 3 (u) ? ?{f 3 (v) | R v ? X}. Let u be a building edge of B and let X and Y be the children of R u . We should consider the following four cases: 1. If f 1 (u) ? ?{f 1 (v) | R v ? X} and f 2 (u) ? ?{f 2 (v) | R v ? X}, then, for any building edge e such that R e ? X, we have f 1 (u) ? f 1 (e) and f 2 (u) ? f 2 (e), ? min(f 1 (u), f 2 (u)). If min(f 1 (e), f 2 (e)) < min, vol.2

, Thus in all cases we have C(f 1 (u), f 2 (u)) ? C(f, p.1

. |-r-v-?-y-}, ) ? ?{f 1 (v) | R v ? X} and f 2 (u) ? ?{f 2 (v)

, Let v be an edge such that R v ? X. By our assumption, we have f 1 (u) ? f 1 (v). Indeed, since f is a one-side increasing map, we can say that either f 1 (u) = f 1 (v) = 0 or f 1 (u) > f 1 (v) because only the watershed-cut edges for ? have non-zero and pairwise distinct weights. If f 1 (u) = f 1 (v) = 0, this implies that neither u nor v are watershed-cut edges for ? and therefore f 2 (u) = f 2 (v) = 0, which implies that f 3 (u) = 0 ? f 3 (v) = 0. Otherwise

. |-r-v-?-x}, Therefore, we have f 3 (u) ? ?{f, vol.3

, C(0, 0) = 0; and

, C(b, a); and 3. if min(a, b) = min(c, d) and max(a, b) < max(c, d) then C(a, b) ? C(c, d)