8.7.1.4 Segmentation, Graph Cuts

Chapter Contents (Back)
Graph Cut. Graph Cut Segmentation.

Boykov, Y.Y.[Yuri Y.], Funka-Lea, G.[Gareth],
Graph Cuts and Efficient N-D Image Segmentation,
IJCV(70), No. 2, November 2006, pp. 109-131.
Springer DOI 0608
Combine boundary regularization with region properties. BibRef

Boykov, Y.Y.[Yuri Y.], Jolly, M.P.[Marie-Pierre],
Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images,
ICCV01(I: 105-112).
IEEE DOI 0106
BibRef
And:
Demonstration of Segmentation with Interactive Graph Cuts,
ICCV01(II: 741).
IEEE DOI 0106
Interactive segmentation. Find a balance between region properties (color) and boundary properties (contrast). BibRef

Peng, B., Veksler, O.,
Parameter Selection for Graph Cut Based Image Segmentation,
BMVC08(xx-yy).
PDF File. 0809
BibRef

Boykov, Y.Y.[Yuri Y.], Veksler, O.[Olga],
Graph cuts in vision and graphics: Theories and applications,
MMCV05(79-96). BibRef 0500

Liu, Y.[Yu], Veksler, O.[Olga],
Animated Classic Mosaics from Video,
ISVC09(II: 1085-1096).
Springer DOI 0911
BibRef

Liu, Y.[Yu], Veksler, O.[Olga], Juan, O.[Olivier],
Simulating Classic Mosaics with Graph Cuts,
EMMCVPR07(55-70).
Springer DOI 0708
BibRef

Veksler, O.[Olga],
Star Shape Prior for Graph-Cut Image Segmentation,
ECCV08(III: 454-467).
Springer DOI 0810
BibRef

Veksler, O.[Olga],
Image Segmentation by Nested Cuts,
CVPR00(I: 339-344).
IEEE DOI 0005
BibRef

Juan, O.[Olivier], Boykov, Y.Y.[Yuri Y.],
Capacity Scaling for Graph Cuts in Vision,
ICCV07(1-8).
IEEE DOI 0710
BibRef
Earlier:
Active Graph Cuts,
CVPR06(I: 1023-1029).
IEEE DOI 0606
BibRef

Boykov, Y.Y.[Yuri Y.], Kolmogorov, V.[Vladimir],
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision,
PAMI(26), No. 9, September 2004, pp. 1124-1137.
IEEE DOI 0409
BibRef
Earlier: EMMCVPR01(359-374).
Springer DOI 0205
Code, Segmentation. BibRef
Earlier:
Computing geodesics and minimal surfaces via graph cuts,
ICCV03(26-33).
IEEE DOI 0311
Combine geodesic active contours with graph cuts.
See also Exact Maximum a Posterori Estimation for Binary Images. Code is available:
WWW Link. Code, Energy Minimization. BibRef

Kolmogorov, V.[Vladimir], Boykov, Y.Y.[Yuri Y.], Rother, C.[Carsten],
Applications of parametric maxflow in computer vision,
ICCV07(1-8).
IEEE DOI 0710
BibRef

Marin, D.[Dmitrii], Tang, M.[Meng], Ben Ayed, I.[Ismail], Boykov, Y.Y.[Yuri Y.],
Kernel Clustering: Density Biases and Solutions,
PAMI(41), No. 1, January 2019, pp. 136-147.
IEEE DOI 1812
BibRef
Earlier: A2, A1, A3, A4:
Normalized Cut Meets MRF,
ECCV16(II: 748-765).
Springer DOI 1611
BibRef
Earlier: A2, A3, A1, A4:
Secrets of GrabCut and Kernel K-Means,
ICCV15(1555-1563)
IEEE DOI 1602
Kernel, Entropy, Standards, Bandwidth, Estimation, Histograms, Decision trees, Kernel methods, kernel clustering, kernel k-means, dominant set. BibRef

Tang, M.[Meng], Marin, D.[Dmitrii], Ben Ayed, I.[Ismail], Boykov, Y.Y.[Yuri Y.],
Kernel Cuts: Kernel and Spectral Clustering Meet Regularization,
IJCV(127), No. 5, May 2019, pp. 477-511.
Springer DOI 1903
Bridge kernel clustering and regularization-based segmentation. BibRef

Boykov, Y.Y.[Yuri Y.], Isack, H.[Hossam], Olsson, C.[Carl], Ben Ayed, I.[Ismail],
Volumetric Bias in Segmentation and Reconstruction: Secrets and Solutions,
ICCV15(1769-1777)
IEEE DOI 1602
Computational modeling BibRef

Bae, E.[Egil], Shi, J., Tai, X.C.[Xue-Cheng],
Graph Cuts for Curvature Based Image Denoising,
IP(20), No. 5, May 2011, pp. 1199-1210.
IEEE DOI 1104

See also Piecewise Constant Level Set Methods and Image Segmentation. BibRef

Bae, E.[Egil], Tai, X.C.[Xue-Cheng],
Efficient Global Minimization Methods for Image Segmentation Models with Four Regions,
JMIV(51), No. 1, January 2015, pp. 71-97.
WWW Link. 1503
BibRef
Earlier:
Efficient Global Minimization for the Multiphase Chan-Vese Model of Image Segmentation,
EMMCVPR09(28-41).
Springer DOI 0908
BibRef
And:
Graph Cut Optimization for the Piecewise Constant Level Set Method Applied to Multiphase Image Segmentation,
SSVM09(1-13).
Springer DOI 0906

See also Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model, A. BibRef

Kolmogorov, V.[Vladimir], Rother, C.[Carsten],
Minimizing Nonsubmodular Functions with Graph Cuts-A Review,
PAMI(29), No. 7, July 2007, pp. 1274-1279.
IEEE DOI 0706
BibRef

Vicente, S.[Sara], Rother, C.[Carsten], Kolmogorov, V.[Vladimir],
Object cosegmentation,
CVPR11(2217-2224).
IEEE DOI 1106
BibRef

Lempitsky, V.[Victor], Blake, A.[Andrew], Rother, C.[Carsten],
Branch-and-Mincut: Global Optimization for Image Segmentation with High-Level Priors,
JMIV(44), No. 3, November 2012, pp. 315-329.
WWW Link. 1209
BibRef

Vicente, S.[Sara], Kolmogorov, V.[Vladimir], Rother, C.[Carsten],
Joint optimization of segmentation and appearance models,
ICCV09(755-762).
IEEE DOI 0909
BibRef
Earlier:
Graph cut based image segmentation with connectivity priors,
CVPR08(1-8).
IEEE DOI 0806
For interactive segmentation. combine segmentation evaluation and appearance evaluation. BibRef

Lempitsky, V.[Victor], Kohli, P.[Pushmeet], Rother, C.[Carsten], Sharp, T.[Toby],
Image segmentation with a bounding box prior,
ICCV09(277-284).
IEEE DOI 0909
BibRef

Lempitsky, V.[Victor], Blake, A.[Andrew], Rother, C.[Carsten],
Image Segmentation by Branch-and-Mincut,
ECCV08(IV: 15-29).
Springer DOI 0810

See also Fusion Moves for Markov Random Field Optimization. BibRef

Delong, A.[Andrew], Boykov, Y.Y.[Yuri Y.],
Globally optimal segmentation of multi-region objects,
ICCV09(285-292).
IEEE DOI 0909
BibRef

Cremers, D.[Daniel], Grady, L.[Leo],
Statistical Priors for Efficient Combinatorial Optimization Via Graph Cuts,
ECCV06(III: 263-274).
Springer DOI 0608
BibRef

Grady, L.[Leo], Sinop, A.K.[Ali Kemal],
Fast approximate Random Walker segmentation using eigenvector precomputation,
CVPR08(1-8).
IEEE DOI 0806
BibRef
Earlier: A2, A1:
A Seeded Image Segmentation Framework Unifying Graph Cuts And Random Walker Which Yields A New Algorithm,
ICCV07(1-8).
IEEE DOI 0710
BibRef

Sinop, A.K.[Ali Kemal], Grady, L.[Leo],
Uninitialized, Globally Optimal, Graph-Based Rectilinear Shape Segmentation: The Opposing Metrics Method,
ICCV07(1-8).
IEEE DOI 0710
BibRef

Lombaert, H.[Herve], Sun, Y.Y.[Yi-Yong], Grady, L.[Leo], Xu, C.Y.[Chen-Yang],
A Multilevel Banded Graph Cuts Method for Fast Image Segmentation,
ICCV05(I: 259-265).
IEEE DOI 0510
BibRef

Boykov, Y.Y.[Yuri Y.], Kolmogorov, V.[Vladimir], Cremers, D.[Daniel], Delong, A.[Andrew],
An Integral Solution to Surface Evolution PDEs Via Geo-cuts,
ECCV06(III: 409-422).
Springer DOI 0608
BibRef
Earlier: A2, A1:
What Metrics Can Be Approximated by Geo-Cuts, Or Global Optimization of Length/Area and Flux,
ICCV05(I: 564-571).
IEEE DOI 0510
BibRef

Xu, N.[Ning], Ahuja, N.[Narendra], Bansal, R.[Ravi],
Object segmentation using graph cuts based active contours,
CVIU(107), No. 3, September 2007, pp. Computer Vision and Image Understanding, Volume 107, Issue 3, September 2007, 210-224.
Elsevier DOI 0709
BibRef
Earlier: A1, A3, A2: CVPR03(II: 46-53).
IEEE DOI 0307
BibRef
Earlier: A1, A2, Only:
Object contour tracking using graph cuts based active contours,
ICIP02(III: 277-280).
IEEE DOI 0210
Object segmentation; Active contours; Snakes; Graph cut BibRef

Komodakis, N.[Nikos], Tziritas, G.[Georgios],
Approximate Labeling via Graph Cuts Based on Linear Programming,
PAMI(29), No. 8, August 2007, pp. 1436-1453.
IEEE DOI 0707
BibRef
Earlier:
A New Framework for Approximate Labeling via Graph Cuts,
ICCV05(II: 1018-1025).
IEEE DOI 0510
BibRef

Kim, J.S.[Jong-Sung], Hong, K.S.[Ki-Sang],
A new graph cut-based multiple active contour algorithm without initial contours and seed points,
MVA(19), No. 3, May 2008, pp. 181-193.
Springer DOI 0803
BibRef

Kohli, P.[Pushmeet], Torr, P.H.S.[Philip H. S.],
Dynamic Graph Cuts for Efficient Inference in Markov Random Fields,
PAMI(29), No. 12, December 2007, pp. 2079-2088.
IEEE DOI 0711
BibRef
Earlier:
Measuring Uncertainty in Graph Cut Solutions: Efficiently Computing Min-marginal Energies Using Dynamic Graph Cuts,
ECCV06(II: 30-43).
Springer DOI 0608
BibRef
Earlier:
Efficiently Solving Dynamic Markov Random Fields Using Graph Cuts,
ICCV05(II: 922-929).
IEEE DOI 0510
mincut/max-flow problem. Given the solution of the max-flow problem on a graph, the dynamic algorithm efficiently computes the maximum flow in a modified version of the graph. Apply to object background segmentation in video. BibRef

Kumar, M.P.[M. Pawan], Torr, P.H.S.[Philip H.S.], Zisserman, A.[Andrew],
OBJCUT: Efficient Segmentation Using Top-Down and Bottom-Up Cues,
PAMI(32), No. 3, March 2010, pp. 530-545.
IEEE DOI 1002
BibRef
Earlier:
Solving Markov Random Fields using Second Order Cone Programming Relaxations,
CVPR06(I: 1045-1052).
IEEE DOI 0606
BibRef
And:
An Object Category Specific MRF for Segmentation,
CLOR06(596-616).
Springer DOI 0711
Automatically obtain the pose, incorporate top-down information in addition to grid. Compare to results from Leibe and Schiele (
See also Robust Object Detection with Interleaved Categorization and Segmentation. ) and Schoenemann and Cremers (
See also Combinatorial Solution for Model-Based Image Segmentation and Real-Time Tracking, A. ). BibRef

Hou, Q.B.[Qi-Bin], Massiceti, D.[Daniela], Dokania, P.K.[Puneet Kumar], Wei, Y.C.[Yun-Chao], Cheng, M.M.[Ming-Ming], Torr, P.H.S.[Philip H. S.],
Bottom-Up Top-Down Cues for Weakly-Supervised Semantic Segmentation,
EMMCVPR17(263-277).
Springer DOI 1805
BibRef

Kohli, P.[Pushmeet], Kumar, M.P.[M. Pawan],
Energy minimization for linear envelope MRFs,
CVPR10(1863-1870).
IEEE DOI 1006
BibRef

Kohli, P.[Pushmeet], Torr, P.H.S.[Philip H.S.],
Measuring uncertainty in graph cut solutions,
CVIU(112), No. 1, October 2008, pp. 30-38.
Elsevier DOI 0810
Parameter learning; Inference; Min-marginals; Graph cuts BibRef

Torr, P.H.S.[Philip H.S.],
Dynamic Markov Random Fields,
IMVIP08(21-26).
IEEE DOI 0809
BibRef

Bugeau, A.[Aurélie], Pérez, P.[Patrick],
Track and Cut: Simultaneous Tracking and Segmentation of Multiple Objects with Graph Cuts,
JIVP(2008), No. 2008, pp. xx-yy.
DOI Link 0811
BibRef
Earlier:
Joint Tracking and Segmentation of Objects Using Graph Cuts,
ACIVS07(628-639).
Springer DOI 0708
BibRef

Zeng, Y.[Yun], Samaras, D.[Dimitris], Chen, W.[Wei], Peng, Q.S.[Qun-Sheng],
Topology cuts: A novel min-cut/max-flow algorithm for topology preserving segmentation in N-D images,
CVIU(112), No. 1, October 2008, pp. 81-90.
Elsevier DOI 0810
Image segmentation; Min-cut/max-flow; Topology preservation; Topology cuts; Graph cuts BibRef

Chai, D.F.[Deng-Feng], Lin, H.W.[Hong-Wei], Peng, Q.S.[Qun-Sheng],
Bisection approach for pixel labelling problem,
PR(43), No. 5, May 2010, pp. 1826-1834.
Elsevier DOI 1003
Pixel labelling; Markov random fields; Graph cut; Stereo BibRef

Chai, D.F.[Deng-Feng],
SQL: Superpixels via quaternary labeling,
PR(92), 2019, pp. 52-63.
Elsevier DOI 1905
Superpixels, Segmentation, Seaming, Pixel labeling, Graph cuts BibRef

Das, P.[Piali], Veksler, O.[Olga], Zavadsky, V.[Vyacheslav], Boykov, Y.Y.[Yuri Y.],
Semiautomatic Segmentation with Compact Shape Prior,
IVC(27), No. 1-2, January 2009, pp. 206-219.
Elsevier DOI 0811
BibRef
Earlier: A1, A2, Only: CRV06(28-28).
IEEE DOI 0607
Based on
See also Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images. Segmentation; Shape prior; Graph cut; Parameter estimation BibRef

Fukuda, K.[Keita], Takiguchi, T.[Tetsuya], Ariki, Y.[Yasuo],
Graph Cuts Segmentation by Using Local Texture Features of Multiresolution Analysis,
IEICE(E92-D), No. 7, July 2009, pp. 1453-1461.
WWW Link. 0907
BibRef

Suga, A.[Akira], Fukuda, K.[Keita], Takiguchi, T.[Tetsuya], Ariki, Y.[Yasuo],
Object recognition and segmentation using SIFT and Graph Cuts,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Han, S.D.[Shou-Dong], Tao, W.B.[Wen-Bing], Wu, X.L.[Xiang-Lin], Tai, X.C.[Xue-Cheng], Wang, T.J.[Tian-Jiang],
Fast image segmentation based on multilevel banded closed-form method,
PRL(31), No. 3, 1 February 2010, pp. 216-225.
Elsevier DOI 1001
Graph Cuts; Closed-form; Multi-seeds; Interactive image segmentation BibRef

Han, S.D.[Shou-Dong], Tao, W.B.[Wen-Bing], Wu, X.L.[Xiang-Lin],
Texture segmentation using independent-scale component-wise Riemannian-covariance Gaussian mixture model in KL measure based multi-scale nonlinear structure tensor space,
PR(44), No. 3, March 2011, pp. 503-518.
Elsevier DOI 1011
Texture segmentation; Graph Cuts; Independent-scale component-wise Riemannian-covariance Gaussian mixture model (ICRGMM); Multi-scale nonlinear structure tensor (MSNST) BibRef

Feng, W.[Wei], Jia, J.Y.[Jia-Ya], Liu, Z.Q.[Zhi-Qiang],
Self-Validated Labeling of Markov Random Fields for Image Segmentation,
PAMI(32), No. 10, October 2010, pp. 1871-1887.
IEEE DOI 1008
Optimize MRF with unknown number of labels. Extend graph cut. Split and merge approach to reduce the problem. BibRef

Feng, W.[Wei], Liu, Z.Q.[Zhi-Qiang],
Self-Validated and Spatially Coherent Clustering with Net-Structured MRF and Graph Cuts,
ICPR06(IV: 37-40).
IEEE DOI 0609
BibRef

El-Zehiry, N.Y.[Noha Youssry], Sahoo, P.[Prasanna], Elmaghraby, A.[Adel],
Combinatorial Optimization of the piecewise constant Mumford-Shah functional with application to scalar/vector valued and volumetric image segmentation,
IVC(29), No. 6, May 2011, pp. 365-381.
Elsevier DOI 1104
Active contours; Graph cuts; Image segmentation BibRef

El-Zehiry, N.Y.[Noha Youssry], Elmaghraby, A.[Adel],
Graph cut based deformable model with statistical shape priors,
ICPR08(1-4).
IEEE DOI 0812
BibRef
And:
A graph cut based active contour without edges with relaxed homogeneity constraint,
ICPR08(1-4).
IEEE DOI 0812
BibRef
And:
A graph cut based active contour for multiphase image segmentation,
ICIP08(3188-3191).
IEEE DOI 0810
BibRef

Tabatabaei, S.S.[Seyed Salim], Coates, M.[Mark], Rabbat, M.[Michael],
GANC: Greedy agglomerative normalized cut for graph clustering,
PR(45), No. 2, February 2012, pp. 831-843.
Elsevier DOI 1110
Graph clustering; Normalized cut; Model order selection; Large scale graphs BibRef

Tao, W.,
Iterative Narrowband-Based Graph Cuts Optimization for Geodesic Active Contours With Region Forces (GACWRF),
IP(21), No. 1, January 2012, pp. 284-296.
IEEE DOI 1112
BibRef

Chen, X., Udupa, J.K., Bagci, U., Zhuge, Y., Yao, J.,
Medical Image Segmentation by Combining Graph Cuts and Oriented Active Appearance Models,
IP(21), No. 4, April 2012, pp. 2035-2046.
IEEE DOI 1204
BibRef

Danek, O.[Ondrej], Matula, P.[Pavel], Maška, M.[Martin], Kozubek, M.[Michal],
Smooth Chan-Vese segmentation via graph cuts,
PRL(33), No. 10, 15 July 2012, pp. 1405-1410.
Elsevier DOI 1205
Image segmentation; Graph cut framework; Chan-Vese model; Memory efficiency; Boundary smoothness
See also Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model, A. BibRef

Liu, Z., Shi, R., Shen, L., Xue, Y., Ngan, K.N., Zhang, Z.,
Unsupervised Salient Object Segmentation Based on Kernel Density Estimation and Two-Phase Graph Cut,
MultMed(14), No. 4, 2012, pp. 1275-1289.
IEEE DOI 1208
BibRef

Wang, Q.[Quan], Boyer, K.L.[Kim L.],
The active geometric shape model: A new robust deformable shape model and its applications,
CVIU(116), No. 12, December 2012, pp. 1178-1194.
Elsevier DOI 1210
Active geometric shape model; Parametric equation; Cubic spline contour; Graph cuts segmentation BibRef

Wang, H.[Hui], Zhang, H.[Hong], Ray, N.[Nilanjan],
Adaptive shape prior in graph cut image segmentation,
PR(46), No. 5, May 2013, pp. 1409-1414.
Elsevier DOI 1302
Template; Adaptive shape prior; Graph cut; Image segmentation BibRef

Wang, H.[Hui], Zhang, H.[Hong],
Adaptive shape prior in graph cut segmentation,
ICIP10(3029-3032).
IEEE DOI 1009
BibRef

Wang, H.[Hui], Ray, N.[Nilanjan], Zhang, H.[Hong],
Graph-cut optimization of the ratio of functions and its application to image segmentation,
ICIP08(749-752).
IEEE DOI 0810
BibRef

Schmitzer, B.[Bernhard], Schnörr, C.[Christoph],
Modelling Convex Shape Priors and Matching Based on the Gromov-Wasserstein Distance,
JMIV(46), No. 1, May 2013, pp. 143-159.
WWW Link. 1303
BibRef
And:
A Hierarchical Approach to Optimal Transport,
SSVM13(452-464).
Springer DOI 1305
BibRef
Earlier:
Weakly Convex Coupling Continuous Cuts and Shape Priors,
SSVM11(423-434).
Springer DOI 1201
BibRef

Schmitzer, B.[Bernhard], Schnörr, C.[Christoph],
Globally Optimal Joint Image Segmentation and Shape Matching Based on Wasserstein Modes,
JMIV(52), No. 3, July 2015, pp. 436-458.
WWW Link. 1506
BibRef

Schmitzer, B.[Bernhard],
A Sparse Multiscale Algorithm for Dense Optimal Transport,
JMIV(56), No. 2, October 2016, pp. 238-259.
WWW Link. 1609
BibRef
Earlier:
A sparse algorithm for dense optimal transport,
SSVM15(629-641).
Springer DOI 1506
BibRef

Fundana, K.[Ketut], Heyden, A.[Anders], Gosch, C.[Christian], Schnorr, C.[Christoph],
Continuous graph cuts for prior-based object segmentation,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Chen, X.J.[Xin-Jian], Udupa, J.K.[Jayaram K.], Alavi, A.[Abass], Torigian, D.A.[Drew A.],
GC-ASM: Synergistic integration of graph-cut and active shape model strategies for medical image segmentation,
CVIU(117), No. 5, May 2013, pp. 513-524.
Elsevier DOI 1303
Object recognition; Image segmentation; Statistical shape models; Graph cut BibRef

Khokher, M.R.[Muhammad Rizwan], Ghafoor, A.[Abdul], Siddiqui, A.M.[Adil Masood],
Image segmentation using multilevel graph cuts and graph development using fuzzy rule-based system,
IET-IPR(7), No. 3, 2013, pp. xx-yy.
DOI Link 1307
BibRef
Earlier:
Image segmentation using fuzzy rule based system and graph cuts,
ICARCV12(1148-1153).
IEEE DOI 1304
BibRef
And:
Multilevel Graph Cuts Based Image Segmentation,
DICTA12(1-8).
IEEE DOI 1303
BibRef

Kechichian, R.[Razmig], Valette, S.[Sebastien], Desvignes, M.[Michel], Prost, R.[Remy],
Shortest-Path Constraints for 3D Multiobject Semiautomatic Segmentation Via Clustering and Graph Cut,
IP(22), No. 11, 2013, pp. 4224-4236.
IEEE DOI 1310
BibRef
Earlier:
Efficient multi-object segmentation of 3D medical images using clustering and graph cuts,
ICIP11(2149-2152).
IEEE DOI 1201
Graph Cut BibRef

Hu, H.[Han], Feng, J.J.[Jian-Jiang], Yu, C.[Chuan], Zhou, J.[Jie],
Multi-Class Constrained Normalized Cut With Hard, Soft, Unary and Pairwise Priors and its Applications to Object Segmentation,
IP(22), No. 11, 2013, pp. 4328-4340.
IEEE DOI 1310
image segmentation
See also Normalized Cuts and Image Segmentation. BibRef

Kéchichian, R.[Razmig], Valette, S.[Sébastien], Desvignes, M.[Michel],
Automatic Multiorgan Segmentation via Multiscale Registration and Graph Cut,
MedImg(37), No. 12, December 2018, pp. 2739-2749.
IEEE DOI 1812
Image segmentation, Feature extraction, Shape, Biomedical imaging, Robustness, Training, Segmentation, graph cut BibRef

Kéchichian, R.[Razmig], Valette, S.[Sébastien], Sdika, M.[Michaël], Desvignes, M.[Michel],
Automatic 3D Multiorgan Segmentation via Clustering and Graph Cut Using Spatial Relations and Hierarchically-Registered Atlases,
MCV14(201-209).
Springer DOI 1501
BibRef

Sashida, S.[Satoshi], Okabe, Y.[Yutaka], Lee, H.K.[Hwee Kuan],
Comparison of multi-label graph cuts method and Monte Carlo simulation with block-spin transformation for the piecewise constant Mumford-Shah segmentation model,
CVIU(119), No. 1, 2014, pp. 15-26.
Elsevier DOI 1402
Mumford-Shah segmentation model BibRef

Sashida, S.[Satoshi], Okabe, Y.[Yutaka], Lee, H.K.[Hwee Kuan],
Application of Monte Carlo simulation with block-spin transformation based on the Mumford-Shah segmentation model to three-dimensional biomedical images,
CVIU(152), No. 1, 2016, pp. 176-189.
Elsevier DOI 1609
Mumford-Shah segmentation model BibRef

Pujadas, E.R., Reisert, M.,
Shape-Based Normalized Cuts Using Spectral Relaxation for Biomedical Segmentation,
IP(23), No. 1, January 2014, pp. 163-170.
IEEE DOI 1402
eigenvalues and eigenfunctions
See also Normalized Cuts and Image Segmentation. BibRef

Duan, J.M.[Jin-Ming], Pan, Z.K.[Zhen-Kuan], Yin, X.F.[Xiang-Feng], Wei, W.B.[Wei-Bo], Wang, G.D.[Guo-Dong],
Some fast projection methods based on Chan-Vese model for image segmentation,
JIVP(2014), No. 1, 2014, pp. 7.
DOI Link 1402
BibRef

Bresson, X.[Xavier], Tai, X.C.[Xue-Cheng], Chan, T.F.[Tony F.], Szlam, A.[Arthur],
Multi-class Transductive Learning Based on L_1 Relaxations of Cheeger Cut and Mumford-Shah-Potts Model,
JMIV(49), No. 1, May 2014, pp. 191-201.
Springer DOI 1404
BibRef

Houhou, N.[Nawal], Bresson, X.[Xavier], Szlam, A.[Arthur], Chan, T.F.[Tony F.], Thiran, J.P.[Jean-Philippe],
Semi-supervised Segmentation Based on Non-local Continuous Min-Cut,
SSVM09(112-123).
Springer DOI 0906
BibRef

Yin, S.[Shibai], Zhao, X.M.[Xiang-Mo], Wang, W.X.[Wei-Xing], Gong, M.L.[Ming-Lun],
Efficient multilevel image segmentation through fuzzy entropy maximization and graph cut optimization,
PR(47), No. 9, 2014, pp. 2894-2907.
Elsevier DOI 1406
Image segmentation BibRef

Yin, S.[Shibai], Qian, Y.M.[Yi-Ming], Gong, M.L.[Ming-Lun],
Unsupervised hierarchical image segmentation through fuzzy entropy maximization,
PR(68), No. 1, 2017, pp. 245-259.
Elsevier DOI 1704
Image segmentation BibRef

Ge, T.Z.[Tie-Zheng], He, K.M.[Kai-Ming], Ke, Q.[Qifa], Sun, J.[Jian],
Optimized Product Quantization,
PAMI(36), No. 4, April 2014, pp. 744-755.
IEEE DOI 1404
BibRef
Earlier:
Optimized Product Quantization for Approximate Nearest Neighbor Search,
CVPR13(2946-2953)
IEEE DOI 1309
Artificial neural networks. nearest neighbor search; product quantization BibRef

Ge, T.Z.[Tie-Zheng], He, K.[Kaiming], Sun, J.[Jian],
Product Sparse Coding,
CVPR14(939-946)
IEEE DOI 1409
BibRef
Earlier:
Graph Cuts for Supervised Binary Coding,
ECCV14(VII: 250-264).
Springer DOI 1408
BibRef

Andrews, S.[Shawn], Changizi, N., Hamarneh, G.[Ghassan],
The Isometric Log-Ratio Transform for Probabilistic Multi-Label Anatomical Shape Representation,
MedImg(33), No. 9, September 2014, pp. 1890-1899.
IEEE DOI 1410
biomedical MRI BibRef

Andrews, S.[Shawn], Hamarneh, G.[Ghassan],
The Generalized Log-Ratio Transformation: Learning Shape and Adjacency Priors for Simultaneous Thigh Muscle Segmentation,
MedImg(34), No. 9, September 2015, pp. 1773-1787.
IEEE DOI 1509
Image segmentation BibRef

Nosrati, M.S.[Masoud S.], Hamarneh, G.[Ghassan],
Local Optimization Based Segmentation of Spatially-Recurring, Multi-Region Objects With Part Configuration Constraints,
MedImg(33), No. 9, September 2014, pp. 1845-1859.
IEEE DOI 1410
image segmentation BibRef

Nosrati, M.S.[Masoud S.], Andrews, S.[Shawn], Hamarneh, G.[Ghassan],
Bounded Labeling Function for Global Segmentation of Multi-part Objects with Geometric Constraints,
ICCV13(2032-2039)
IEEE DOI 1403
Global segmentation BibRef

Andrews, S.[Shawn], McIntosh, C.[Chris], Hamarneh, G.[Ghassan],
Convex multi-region probabilistic segmentation with shape prior in the isometric log-ratio transformation space,
ICCV11(2096-2103).
IEEE DOI 1201
BibRef

Rao, J.[Josna], Abu-Gharbieh, R.[Rafeef], Hamarneh, G.[Ghassan],
Adaptive Regularization for Image Segmentation Using Local Image Curvature Cues,
ECCV10(IV: 651-665).
Springer DOI 1009
BibRef
Earlier: A1, A3, A2:
Adaptive Contextual Energy Parameterization for Automated Image Segmentation,
ISVC09(I: 1089-1100).
Springer DOI 0911
BibRef

Hamarneh, G.[Ghassan],
Multi-label MRF Optimization via a Least Squares s-t Cut,
ISVC09(I: 1055-1066).
Springer DOI 0911
BibRef

Peng, Y.[Yi], Chen, L.[Li], Ou-Yang, F.X.[Fang-Xin], Chen, W.[Wei], Yong, J.H.[Jun-Hai],
JF-Cut: A Parallel Graph Cut Approach for Large-Scale Image and Video,
IP(24), No. 2, February 2015, pp. 655-666.
IEEE DOI 1502
computational complexity BibRef

Wang, X., Tang, Y., Masnou, S., Chen, L.,
A Global/Local Affinity Graph for Image Segmentation,
IP(24), No. 4, April 2015, pp. 1399-1411.
IEEE DOI 1503
Bipartite graph BibRef

Tuysuzoglu, A., Karl, W.C., Stojanovic, I., Castanon, D., Unlu, M.S.,
Graph-Cut Based Discrete-Valued Image Reconstruction,
IP(24), No. 5, May 2015, pp. 1614-1627.
IEEE DOI 1504
Approximation methods BibRef

Fix, A.[Alexander], Gruber, A.[Aritanan], Boros, E.[Endre], Zabih, R.[Ramin],
A Hypergraph-Based Reduction for Higher-Order Binary Markov Random Fields,
PAMI(37), No. 7, July 2015, pp. 1387-1395.
IEEE DOI 1506
BibRef
Earlier:
A graph cut algorithm for higher-order Markov Random Fields,
ICCV11(1020-1027).
IEEE DOI 1201
BibRef

Wang, C.[Chen], Zabih, R.[Ramin],
Relaxation-Based Preprocessing Techniques for Markov Random Field Inference,
CVPR16(5830-5838)
IEEE DOI 1612
BibRef

Fix, A.[Alexander], Wang, C.[Chen], Zabih, R.[Ramin],
A Primal-Dual Algorithm for Higher-Order Multilabel Markov Random Fields,
CVPR14(1138-1145)
IEEE DOI 1409
Markov Random Fields; Optimization; primal-dual algorithms BibRef

Wang, T.[Tao], Ji, Z.X.[Ze-Xuan], Sun, Q.S.[Quan-Sen], Chen, Q.A.[Qi-Ang], Han, S.D.[Shou-Dong],
Image segmentation based on weighting boundary information via graph cut,
JVCIR(33), No. 1, 2015, pp. 10-19.
Elsevier DOI 1512
Image segmentation BibRef

Wang, T.[Tao], Sun, Q.S.[Quan-Sen], Ji, Z.X.[Ze-Xuan], Chen, Q.A.[Qi-Ang], Fu, P.[Peng],
Multi-layer graph constraints for interactive image segmentation via game theory,
PR(55), No. 1, 2016, pp. 28-44.
Elsevier DOI 1604
Image segmentation BibRef

Wang, T.[Tao], Ji, Z.X.[Ze-Xuan], Sun, Q.S.[Quan-Sen], Chen, Q.A.[Qi-Ang], Jing, X.Y.,
Interactive Multilabel Image Segmentation via Robust Multilayer Graph Constraints,
MultMed(18), No. 12, December 2016, pp. 2358-2371.
IEEE DOI 1612
Computational complexity BibRef

Ji, Z.X.[Ze-Xuan], Huang, Y.[Yubo], Sun, Q.S.[Quan-Sen], Cao, G.[Guo],
A spatially constrained generative asymmetric Gaussian mixture model for image segmentation,
JVCIR(40, Part B), No. 1, 2016, pp. 611-626.
Elsevier DOI 1610
Asymmetric Gaussian mixture model BibRef

Ji, Z.X.[Ze-Xuan], Liu, J.Y.[Jin-Yao], Yuan, H.D.[Heng-Dong], Huang, Y.[Yubo], Sun, Q.S.[Quan-Sen],
A Spatially Constrained Asymmetric Gaussian Mixture Model for Image Segmentation,
PSIVT15(697-708).
Springer DOI 1602
BibRef

Arias-Lorza, A.M.[Andres M.], Petersen, J.[Jens], van Engelen, A.[Arna], Selwaness, M.[Mariana], van der Lugt, A., Niessen, W.J., de Bruijne, M.,
Carotid Artery Wall Segmentation in Multispectral MRI by Coupled Optimal Surface Graph Cuts,
MedImg(35), No. 3, March 2016, pp. 901-911.
IEEE DOI 1603
Bifurcation BibRef

Arias-Lorza, A.M.[Andres M.], Petersen, J.[Jens], van Engelen, A.[Arna], Tang, H.[Hui], Selwaness, M.[Mariana],
Carotid Artery Wall Segmentation by Coupled Surface Graph Cuts,
MCVM12(38-47).
Springer DOI 1305
BibRef

Fishbain, B., Hochbaum, D.S.[Dorit S.], Mueller, S.[Stefan],
A competitive study of the pseudoflow algorithm for the minimum s-t cut problem in vision applications,
RealTimeIP(11), No. 3, March 2016, pp. 589-609.
Springer DOI 1604
BibRef

Zhong, Y.F.[Yan-Fei], Gao, R.R.[Rong-Rong], Zhang, L.P.[Liang-Pei],
Multiscale and Multifeature Normalized Cut Segmentation for High Spatial Resolution Remote Sensing Imagery,
GeoRS(54), No. 10, October 2016, pp. 6061-6075.
IEEE DOI 1610
geophysical image processing BibRef

Zheng, Z.[Zhuo], Zhong, Y.F.[Yan-Fei], Wang, J.[Junjue], Ma, A.[Ailong], Zhang, L.P.[Liang-Pei],
FarSeg++: Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery,
PAMI(45), No. 11, November 2023, pp. 13715-13729.
IEEE DOI 2310
BibRef

Han, X.B.[Xiao-Bing], Zhong, Y.F.[Yan-Fei], Zhang, L.P.[Liang-Pei],
An Efficient and Robust Integrated Geospatial Object Detection Framework for High Spatial Resolution Remote Sensing Imagery,
RS(9), No. 7, 2017, pp. xx-yy.
DOI Link 1708
BibRef

Zheng, Z.[Zhuo], Zhong, Y.F.[Yan-Fei], Ma, A.L.[Ai-Long], Han, X.B.[Xiao-Bing], Zhao, J.[Ji], Liu, Y.F.[Yan-Fei], Zhang, L.P.[Liang-Pei],
HyNet: Hyper-Scale Object Detection Network Framework for Multiple Spatial Resolution Remote Sensing Imagery,
PandRS(166), 2020, pp. 1-14.
Elsevier DOI 2007
Hyper-scale feature representation, Convolutional neural network, Multiple spatial resolution, Remote sensing BibRef

Han, X.B.[Xiao-Bing], Zhong, Y.F.[Yan-Fei], Cao, L.Q.[Li-Qin], Zhang, L.P.[Liang-Pei],
Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification,
RS(9), No. 8, 2017, pp. xx-yy.
DOI Link 1708
BibRef

Zheng, Z.[Zhuo], Zhong, Y.F.[Yan-Fei], Wang, J.J.[Jun-Jue], Ma, A.L.[Ai-Long],
Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery,
CVPR20(4095-4104)
IEEE DOI 2008
Semantics, Geospatial analysis, Remote sensing, Object segmentation, Image segmentation, Optimization, Feature extraction BibRef

Yu, M., Shen, S., Hu, Z.,
Dynamic Parallel and Distributed Graph Cuts,
IP(25), No. 12, December 2016, pp. 5511-5525.
IEEE DOI 1612
computer vision BibRef

Yu, M., Shen, S., Hu, Z.,
Dynamic Graph Cuts in Parallel,
IP(26), No. 8, August 2017, pp. 3775-3788.
IEEE DOI 1707
graph theory, image segmentation, parallel processing, video signal processing, GrabCut, MRF models, foreground-background segmentation, max-flow problem, parallel computation, parallel dynamic graph cuts, video segmentation, Algorithm design and analysis, Data structures, Heuristic algorithms, Merging, Parallel algorithms, Particle separators, Dynamic graph cuts, GrabCut, parallel computation, video, segmentation BibRef

Sangüesa, A.A.[Adrià A.], Jorgensen, N.K.[Nicolai K.], Larsen, C.A.[Christian A.], Nasrollahi, K.[Kamal], Moeslund, T.B.[Thomas B.],
Initiating GrabCut by color difference for automatic foreground extraction of passport imagery,
IPTA16(1-6)
IEEE DOI 1703
image segmentation BibRef

Holuša, M.[Michael], Sojka, E.[Eduard],
The k-max distance in graphs and images,
PRL(98), No. 1, 2017, pp. 103-109.
Elsevier DOI 1710
BibRef
Earlier:
A k-max Geodesic Distance and Its Application in Image Segmentation,
CAIP15(I:618-629).
Springer DOI 1511
BibRef
Earlier:
Image Segmentation Using Iterated Graph Cuts with Residual Graph,
ISVC13(I:228-237).
Springer DOI 1310
BibRef
Earlier:
Object Detection from Multiple Images Based on the Graph Cuts,
ISVC12(I: 262-271).
Springer DOI 1209
Distance measuring BibRef

Holuša, M.[Michael], Sukhanov, A.[Andrey], Sojka, E.[Eduard],
Image Segmentation Based on Solving the Flow in the Mesh with the Connections of Limited Capacities,
ICIAR17(163-170).
Springer DOI 1706
BibRef

Lu, Y.W.[Yu-Wei], Jiang, J.G.[Jian-Guo], Qi, M.B.[Mei-Bin], Zhan, S.[Shu], Yang, J.[Jie],
Segmentation method for medical image based on improved GrabCut,
IJIST(27), No. 4, 2017, pp. 383-390.
DOI Link 1712
figure of foreground, Gaussian Mixture model, GrabCut algorithm, image segmentation, training set BibRef

Essa, E.[Ehab], Xie, X.H.[Xiang-Hua],
Automatic segmentation of cross-sectional coronary arterial images,
CVIU(165), No. 1, 2017, pp. 97-110.
Elsevier DOI 1712
IVUS BibRef

Essa, E.[Ehab], Xie, X.H.[Xiang-Hua], Sazonov, I.[Igor], Nithiarasu, P.[Perumal],
Automatic IVUS media-adventitia border extraction using double interface graph cut segmentation,
ICIP11(69-72).
IEEE DOI 1201
BibRef

Essa, E.[Ehab], Xie, X.H.[Xiang-Hua], Sazonov, I.[Igor], Nithiarasu, P.[Perumal], Smith, D.[Dave],
Shape Prior Model for Media-Adventitia Border Segmentation in IVUS Using Graph Cut,
MCVM12(114-123).
Springer DOI 1305
BibRef

Jones, J.L.[Jonathan-Lee], Essa, E.[Ehab], Xie, X.H.[Xiang-Hua],
Interactive Segmentation of Media-Adventitia Border in IVUS,
CAIP13(II:466-474).
Springer DOI 1311
BibRef

Cheng, Z.[Ziang], Liu, Y.[Yang], Liu, G.J.[Guo-Jun],
A new primal-dual algorithm for multilabel graph-cuts problems with approximate moves,
CVIU(165), No. 1, 2017, pp. 75-84.
Elsevier DOI 1712
MRF-MAP inference BibRef

Furuta, R.[Ryosuke], Tsubaki, I.[Ikuko], Yamasak, T.[Toshihiko],
Fast Volume Seam Carving With Multipass Dynamic Programming,
CirSysVideo(28), No. 5, May 2018, pp. 1087-1101.
IEEE DOI 1805
BibRef
Earlier: ICIP16(1818-1822)
IEEE DOI 1610
Heuristic algorithms, Image quality, Memory management, Minimization, Surface treatment, video retargeting Cost function. Graph cut process is expensive. BibRef

Zheng, Q.A.[Qi-Ang], Li, H.L.[Hong-Lun], Fan, B.D.[Bao-De], Wu, S.H.[Shuan-Hu], Xu, J.D.[Jin-Dong],
Integrating support vector machine and graph cuts for medical image segmentation,
JVCIR(55), 2018, pp. 157-165.
Elsevier DOI 1809
Support vector machine, Graph cuts, Medical image segmentation BibRef

Peng, Y.[Yan], Zhang, Z.M.[Zhao-Ming], He, G.J.[Guo-Jin], Wei, M.Y.[Ming-Yue],
An Improved GrabCut Method Based on a Visual Attention Model for Rare-Earth Ore Mining Area Recognition with High-Resolution Remote Sensing Images,
RS(11), No. 8, 2019, pp. xx-yy.
DOI Link 1905
BibRef

Ke, X.[Xin], He, G.J.[Guo-Jin],
Visual attention based model for target detection in high resolution remote sensing images,
CVRS12(84-89).
IEEE DOI 1302
aircraft in Quick-Bird images. BibRef

Bejar, H.H.C.[Hans H.C.], Ferzoli Guimaraes, S.J.[Silvio Jamil], Miranda, P.A.V.[Paulo A.V.],
Efficient hierarchical graph partitioning for image segmentation by optimum oriented cuts,
PRL(131), 2020, pp. 185-192.
Elsevier DOI 2004
Unsupervised segmentation, Hierarchical graph partitioning, Graph-cut measure BibRef

de Moraes Braz, C.[Caio], Miranda, P.A.V.[Paulo A. V.], Ciesielski, K.C.[Krzysztof Chris], Cappabianco, F.A.M.[Fábio A. M.],
Optimum Cuts in Graphs by General Fuzzy Connectedness with Local Band Constraints,
JMIV(62), No. 5, June 2020, pp. 659-672.
Springer DOI 2007
BibRef

Wang, F.Q.[Fa-Qiang], Zhao, C.C.[Cui-Cui], Liu, J.[Jun], Huang, H.Y.[Hai-Yang],
A Variational Image Segmentation Model Based on Normalized Cut with Adaptive Similarity and Spatial Regularization,
SIIMS(13), No. 2, 2020, pp. 651-684.
DOI Link 2007
BibRef

Lermé, N.[Nicolas], Le Hégarat-Mascle, S.[Sylvie], Malgouyres, F.[François], Lachaize, M.[Marie],
Multilayer Joint Segmentation Using MRF and Graph Cuts,
JMIV(62), No. 6-7, July 2020, pp. 961-981.
Springer DOI 2007
BibRef

Zhu, H., Zhang, J., Xu, G., Deng, L.,
Tensor Field Graph-Cut for Image Segmentation: A Non-Convex Perspective,
CirSysVideo(31), No. 3, March 2021, pp. 1103-1113.
IEEE DOI 2103
Tensile stress, Image segmentation, Optimization, Image edge detection, Shape, Anisotropic magnetoresistance, hypergraph cut BibRef

Jia, L.[Lu], Zhang, T.T.[Tian-Tian], Fang, J.[Jing], Dong, F.[Feibiao],
Multiple Kernel Graph Cut for SAR Image Change Detection,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Chakraborty, S.[Sujoy], Kirchner, M.[Matthias],
Sensor-based image manipulation localization with Discriminative Random fields and Graph Cut,
JVCIR(80), 2021, pp. 103273.
Elsevier DOI 2110
Photo-response non-uniformity, Discriminative random field, Manipulation localization, Correlation predictor BibRef

Zhu, L.[Lei], Kang, X.J.[Xue-Jing], Ye, L.[Lizhu], Ming, A.[Anlong],
Explored Normalized Cut With Random Walk Refining Term for Image Segmentation,
IP(31), No. 2022, pp. 2893-2906.
IEEE DOI 2204
Image segmentation, Image edge detection, Task analysis, Computational modeling, Analytical models, graph signal processing BibRef

Wang, C.[Chen], Chen, X.J.[Xiao-Jun], Nie, F.P.[Fei-Ping], Huang, J.Z.[Joshua Zhexue],
Directly solving normalized cut for multi-view data,
PR(130), 2022, pp. 108809.
Elsevier DOI 2206
Clustering, Graph cut, Multi-view BibRef

Wang, Y.T.[Yang-Tao], Shen, X.[Xi], Yuan, Y.[Yuan], Du, Y.M.[Yu-Ming], Li, M.[Maomao], Hu, S.X.[Shell Xu], Crowley, J.L.[James L.], Vaufreydaz, D.[Dominique],
TokenCut: Segmenting Objects in Images and Videos With Self-Supervised Transformer and Normalized Cut,
PAMI(45), No. 12, December 2023, pp. 15790-15801.
IEEE DOI 2311
BibRef

Wang, Y.T.[Yang-Tao], Shen, X.[Xi], Hu, S.X.[Shell Xu], Yuan, Y.[Yuan], Crowley, J.L.[James L.], Vaufreydaz, D.[Dominique],
Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut,
CVPR22(14523-14533)
IEEE DOI 2210
Visualization, Image segmentation, Image edge detection, Object detection, Self-supervised learning, Transformers, Self- semi- meta- Recognition: detection BibRef

Jenner, E.[Erik], Sanmartín, E.F.[Enrique Fita], Hamprecht, F.A.[Fred A.],
Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice,
ICCV21(4582-4591)
IEEE DOI 2203
Image segmentation, Machine learning algorithms, Runtime, Machine learning, Semisupervised learning, Minimization, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Sun, J.X.[Jun-Xiao], Zhang, Y.[Yan], Zhu, J.[Jian], Wu, J.S.[Jia-Song], Kong, Y.Y.[You-Yong],
Semi-Supervised Medical Image Semantic Segmentation with Multi-scale Graph Cut Loss,
ICIP21(624-628)
IEEE DOI 2201
Image segmentation, Annotations, Semantics, Semisupervised learning, Convolutional neural networks, Semantic Segmentation BibRef

Zheng, Z.[Zhou], Oda, M.[Masahiro], Mori, K.[Kensaku],
Graph Cuts Loss to Boost Model Accuracy and Generalizability for Medical Image Segmentation,
CVAMD21(3297-3306)
IEEE DOI 2112
Image segmentation, Supervised learning, Liver, Computer architecture, Cost function BibRef

Cahill, N.D., Hayes, T.L., Meinhold, R.T., Hamilton, J.F.,
Compassionately Conservative Balanced Cuts for Image Segmentation,
CVPR18(1683-1691)
IEEE DOI 1812
Image segmentation, Minimization, Linear programming, Indexes, Partitioning algorithms BibRef

Aykut, M.[Murat], Akturk, S.M.[Saffet Murat],
An Improvement on GrabCut with CLAHE for the Segmentation of the Objects with Ambiguous Boundaries,
ICIAR18(116-122).
Springer DOI 1807
BibRef

Chen, X., Haung, J.Z., Nie, F., Chen, R., Wu, Q.,
A Self-Balanced Min-Cut Algorithm for Image Clustering,
ICCV17(2080-2088)
IEEE DOI 1802
content-based retrieval, eigenvalues and eigenfunctions, image retrieval, image segmentation, iterative methods, Software algorithms BibRef

Kirillov, A., Levinkov, E., Andres, B., Savchynskyy, B., Rother, C.[Carsten],
InstanceCut: From Edges to Instances with MultiCut,
CVPR17(7322-7331)
IEEE DOI 1711
Automobiles, Image edge detection, Image segmentation, Pipelines, Proposals, Semantics BibRef

Brzoza, A., Muszynski, G.,
An approach to image segmentation based on shortest paths in graphs,
WSSIP17(1-5)
IEEE DOI 1707
Benchmark testing, Gaussian distribution, Histograms, Image segmentation, Measurement, Synchronous digital hierarchy, Graph theory, Image Texture Analysis, Image processing, Image segmentation, Shortest, path, problem BibRef

Fuse, T., Harada, R.,
Development Of Image Selection Method Using Graph Cuts,
ISPRS16(B5: 641-646).
DOI Link 1610
BibRef

Cheng, D., Meng, G., Pan, C.,
Sea-land segmentation via hierarchical region merging and edge directed graph cut,
ICIP16(1274-1278)
IEEE DOI 1610
Image color analysis BibRef