Please wait a minute...
Computational Visual Media  2020, Vol. 6 Issue (4): 355-384    doi: 10.1007/s41095-020-0177-5
Review Article     
A survey of recent interactive image segmentation methods
Hiba Ramadan1,(✉)(),Chaymae Lachqar2,Hamid Tairi1
1 Department of Informatics, Faculty of Sciences Dhar El Mahraz, University of Sidi Mohamed Ben Abdellah, Fez 30000, Morocco
2 Faculty of Medicine and Pharmacy, University of Sidi Mohamed Ben Abdellah, Fez 30000, Morocco
Download: PDF (719 KB)      HTML  
Export: BibTeX | EndNote (RIS)      

Abstract  

Image segmentation is one of the most basic tasks in computer vision and remains an initial step of many applications. In this paper, we focus on interactive image segmentation (IIS), often referred to as foreground-background separation or object extraction, guided by user interaction. We provide an overview of the IIS literature by covering more than 150 publications, especially recent works that have not been surveyed before. Moreover, we try to give a comprehensive classification of them according to different viewpoints and present a general and concise comparison of the most recent published works. Furthermore, we survey widely used datasets, evaluation metrics, and available resources in the field of IIS.



Key wordsinteractive image segmentation      user interaction      label propagation      deep learning      superpixels     
Received: 12 March 2020      Published: 30 November 2020
Corresponding Authors: Hiba Ramadan     E-mail: hiba.ramadan@usmba.ac.ma
About author: Hiba Ramadan received her Ph.D. degree in 2018 from the University Sidi Mohamed Ben Abdellah (USMBA), Fez, Morocco. Her research interests are in video analysis, medical imaging, visual information retrieval, and pattern recognition.|Chaymae Lachqar is a medical student in Faculty of Medecine and Pharmacy, USMBA, Fez. She is interested in biotechnology and bioinformatics.|Hamid Tairi received his Ph.D. degree in 2001 from USMBA, Morocco. In 2002 he has been a postdoc in the Image Processing Group of the Laboratory in France. Since 2003, he has been an associate professor at USMBA, where he obtained his HDR in 2009. His research interests are in 3D reconstruction of artificial vision, medical imaging, visual information retrieval, and pattern recognition.
Cite this article:

Hiba Ramadan,Chaymae Lachqar,Hamid Tairi. A survey of recent interactive image segmentation methods. Computational Visual Media, 2020, 6(4): 355-384.

URL:

http://cvm.tsinghuajournals.com/10.1007/s41095-020-0177-5     OR     http://cvm.tsinghuajournals.com/Y2020/V6/I4/355

Fig. 1 Categories of IIS methods according to various criteria.
Fig. 2 Different modalities of user input in IIS. (a) Boundary seeds. (b) Region seeds, FG, and BG points. (c) Region seeds, FG, and BG strokes/scribbles. (d) ROI, drawn. (e) ROI, loose BB. (f) ROI, tight BB.
DatasetDescription
Berkeley segmentation data set (BSDS) [215]Large dataset of natural images with human annotation to serve as ground-truth. Benchmark to evaluate different contour detection and image segmentation algorithms. BSDS500 is the latest version and contains 500 natural images with their ground-truth.
Microsoft GrabCut dataset [79]Dataset for IIS containing 50 color images. Ground-truth is stored as tri-maps identifying FG, BG, and mixed pixels (unknown).
MSRA10K dataset [216]Dataset consisting of 10,000 images with pixel accurate salient object labeling. Proposed to evaluate salient object detection and segmentation methods.
Pascal VOC datasets [217]Data sets from the VOC challenges(1) providing standardized image data sets for object class recognition; includes image annotations.
Alpha matting dataset [218]High-quality matting database that extends the dataset of Ref. [195] by adding challenging images from natural scenes.
Icoseg dataset [115]Large challenging dataset with 643 images proposed to evaluate image co-segmentation methods. It contains 38 groups of images from real scenes. Each group consists of instances of similar objects.
Weizmann dataset [219]Segmentation evaluation dataset containing 200 gray level images with ground-truth segmentations.
Microsoft COCO [220]Dataset for detecting and segmenting objects containing a vast collection of object instances with a total of 2,500,000 labeled instances in 328,000 images.
Cityscapes [221]Dataset for semantic urban scene understanding containing 5000 finely annotated images of driving scenes, including 2975 images for training, 500 for validation, and 1525 for testing. Eight object classes are provided with per-instance annotation.
Kitti [222]Suite of vision components for an autonomous driving platform. The object detection dataset contains 7481 training images annotated with 3D bounding boxes. A full description of the annotations can be found in the readme of the object development kit readme on the Kitti homepage(2). Pixel-level annotation of a subset of images from the dataset is provided by Ref. [223].
Table 1 Different datasets used in the evaluation of IIS methods
MetricDescriptionFormulaeAlias
Error rate (E?R?R)Fraction or percentage of wrongly classified pixels in ROIErr=|SG-G|/(nb.ofpixelsinROI)Mis-segmentation rate
True Positive Rate (T?P?R)Measures of FG and BG assignment accuracyT?P?R=|SG|/|G|Recall
False Negative Rate (F?N?R)F?N?R=1-T?P?R
False Positive Rate (F?P?R)F?P?R=|SG-G|/|G|
True Negative Rate (T?N?R)T?N?R=1-F?P?R
Precision (P)P=|SG|/|S|
F-measure (F)F=(2×R×P)/(R+P)F-score
Intersection over Union (IoU)Number of correctly labelled FG/BG pixels to the number of pixels labelled as FG or BG in either G or SI?o?U=|SG|/|SG|Jaccard index, overlap coefficient
Dice Similarity Coefficient (DSC)Overlap between the two segmented object volumes G and SD?S?C=2×|SG|/(|S|+|G|)
Rand Index (RI)Fraction of pixel pairs whose labels are consistent between G and S[224]
Global Consistency Error (GCE)Measure of the extent to which one segmentation can be viewed as a refinement of the other[215]
Boundary Displacement Error (BDE)Average displacement error of boundary pixels between G and S[225]
Variation of Information (VI)Measure of the distance between the two segmentations G and S using average conditional entropy[226]
Segmentation Covering (SC)Quality measure of the covering of the segmentation S by G[208]
Modified Hausdorff Distance (MHD)Measure of the displacement between the segmentation S and G[227]
Number of Clicks (NoCs)Mean number of clicks required to achieve a certain I?o?U[23]
F-Boundary Score (FB)Precision/recall for the boundaries of G and S[228]
Table 2 Different measures used in the literature to evaluate IIS methods
MethodologyWorkYearKeywordsProcessing levelUser interactionEvaluation metricsAdvantagesLimitations
Contour-based methods[122]2018Level set; multi-phase formulationPxSeedsIoUSuccessful segmentation of images with intensity inhomogeneity and a high level of noise
[74]2018Level set; kernel descriptorPxSeedsRI, CGE, VoI, SCRobustness in heterogeneous and cluttered images
[136]2020Intensity inhomogeneity; Bayesian criterion; Markov randomPxSeedsIoU, runtimeRobustness to different types of noise and initializations; superior to other well-known ACM with respect to the balance of segmentation accuracy and speedNeed to experimentally set several control parameters
GC-based methodsPoint-Cut [82]2016One point; object proposalsPx/SpxSeedsDSCCompetitive results using only one input seed pointDependence on the quality of object proposals; failure in case of highly textured FG objects
NC-Cut [8]2016NC; topologyPx/SpxROIIoU, Err, RI, GCE, BDEMuch less sensitive to a loose ROI than other methodsFailure when an image has high measure of average indeterminacy
Loose-Cut [81]2017Label consistency; global similarity constraintPx/SpxROIErr, F-measureOutperforms other methods given a loose BB
Super-Cut [83]2017Superpixel clustering; local similarity constraintPx/SpxROIIoU, F-measureReduced time cost due to the one cut segmentation; accurate labelling due to clustering of pixels and superpixelsFailure when the input BB is much tighter than the ground-truth
[211]2018Two phase; region-level-and pixel-level-segmentationPx/SpxROIErrImprovement of GrabCut extents methods using two phasesSensitivity of region-level segmentation phase to the tightness of the input ROI
[64]2018Diffusion likelihood; superpixel-based groupingPx/SpxSeedsIoU, Err, RI, VoI, runtimeAccurate prediction of label probabilities from few seeds; enforcing continuity for the object segmentation by using Spx grouping
[150]2019Geodesic GC; one cutPxSeedsIoU, DSC, ErrImprovement of geodesic GC and One cut with little additional runtime and low user interaction
RW-based methodsSRW [161]2016RW; complex texturePxSeedsErr, IoU, runtimeAccurate segmentation of images with complex textureSlower than other methods
[100]2017Structure-aware labeling; occurrence and co-occurrence probabilityPxSeedsDSCRobust to inaccurate initial labelsFailure when initial labels have similar color distributions
[165]2018Iterative boundary RW; feed-back systemPxSeedsErrFeedback system that allows the computer to exploit and understand the intent of limited user input
Deep learning-based methods[23]2016FCN; GCPxSeedsIoU, NoCsFirst work solving IIS using DL model; high quality segmentations compared to other methods for few clicksFailure to correct the generated prediction by producing similar outputs regardless of additional clicks added
[179]2017ROI proposalsPxSeedsIoU, NoCsReduction of the amount of user interaction required for accurate segmentationFailure in cases of occluded or hairy objects
[17]2017Extreme pointsPxSeedsErr, NoCsReduced user input; achieves state-of-the-art results for various benchmarks and datasetsRestriction to using four clicks to generate segmentations; difficult to refine results with additional clicks when the quality of the segmentation is low
[182]2017Polygon-RNNPxSeedsIoU, NoCsFacilitation of the annotation task by treating object segmentation as a polygon prediction task; easy to incorporate user corrections to improve segmentationOutput with low resolution (28×28) producing blocky polygons for large objects
[22]2018Iterative trainingPxSeedsIoU, NoCsRobust to variations in clicks; significant improvement over state of the art; needs 0.2 clicks fewer than DEXTR [17]Some failures to remove unwanted objects with a few clicks
[21]2018Seed generation; deep Reinforcement learningPxSeedsIoU, NoCsSignificant reduction of human effort for the IIS task
[24]2018Coupled CNN architecture; clicksPxSeedsIoU, Err, NoCs, runtime2.3 times faster than [21]; reduction of user-guided segmentation to a forward pass in a CNN
[180]2018FCN; two-stream networkPxSeedsIoU, NoCsImproved performance over single-stream networks
[183]2018Polygon-RNN++; RNN; reinforcement learningPxSeedsIoU, NoCsEffective tool for fast and accurate object annotation in challenging datasets; improvement of the polygon-RNN model [182]Annotation of a single object and failure in cases of multi-component objects
[106]2019Backpropagating refinement scheme (BRS)PxSeedsIoU, NoCsBetter performance for hard cases due to the use of optimization-based backpropagation refinement; possibility to apply backpropagating refinement to other computer vision tasksComputational cost per click is high due to running forward and backward passes through a deep network multiple times; not practical for many end user scenarios
[107]2019Mask R-CNNPxSeedsIoU, NoCsEfficient for both single object and full image segmentation
[188]2019Scale-diversity; non-maximum suppressionPxSeedsIoU, NoCs, runtimeCapability to produce multiple diverse and semantically meaningful segmentations
[181]2019Content-aware guidance mapsPx/SpxSeedsIoU, NoCsUsing guidance maps, even the most basic FCNs are able to outperform state-of-the-arts DL-based methodsFailure for some challenging scenarios (objects with holes, occlusion)
[184]2019Curve-GCNPxSeedsIoU, NoCs, FBEfficient annotation for both line-based and curved objects, by either polygons or splines; 100x faster than Polygon-RNN++ [183]
[185]2019Deep extreme level setPxSeedsIoU, NoCs, FBRobust boundary object extraction even in the presence of noise
[189]2020First click attention network; FCNPxSeedsIoU, NoCsSuperiority of the proposed method for various datasetsFailure when the desired object may not be clicked by the user due to structure or occlusion
[186]2020Feature-BRSPxSeedsIoU, NoCs, runtimeImprovement of the original BRS by running forward and backward passes just for a small part of a network; high performance in terms of speed and accuracy
OthersACP-Cut [43]2016Discriminative learning; seed propagationSpxSeedsTPR, TNR, runtimeImproved segmentation resultsFailure when Spx share similar characteristics in both FG and BG
[202]2016Multi-layer graph; non-parametric learning; game theoryPx/SpxSeedsErr, IoU, runtimeImproved segmentation accuracy by exploring relationships between Px/Spx and labelsLess robust for practical applications because of the requirement to set several parameters experimentally
Dominant Sets [199]2016Dominant sets; quadratic optimizationSpxSeeds/ROIIoU, Err, DSCFlexible to input modalities (BB, loose BB, scribbles only on the ROI and scribbles with error)Pre-processing step using the original UCM [180] is very slow for a practical tool
[46]2017Samples reconstruction; FLDASpxSeedsErr, TPR, FPRReduction of sensitivity to number of seeds and locations, captures long range grouping cuesDependete on high quality superpixel over-segmentation; failure when image contains hairy objects or other objects with complex edges
[33]2018Global affinity; graph diffusionPxSeedsErr, runtimeBetter results when FG and BG have similar appearances than for other local affinity graph-based models (GC and RW-based methods)Less robust for practical applications because of need to set the diffusion control parameter experimentally
[104]2018Manifold ranking; machine assistedSpxSeeds/ROIDCSFlexible to different user interactions (ROI/seeds/true-or-false feed-back); practical for real-time use
[231]2019Likelihood learning; probabilistic estimationPxSeedsRI, VoI, Err, seed quantity and locationImprovement over results of latest IIS methods including some DL-based one
[201]2019Label propagation; complex networksPxSeedsErr, runtimeSimplicity; low time; multi-class
[103]2019Region proposals; boundary click; objectness score; user votesSpxSeedsIoU, NoCs, runtimeSuitable for both static and dynamic scenes; only a few clicks are needed for accurate object segmentation; competitive results against a recent DL-based method [183]Dependent on the quality of the generated region proposals
Table 3 General comparison of recent IIS works. Evaluation metrics ae described in Table 2
GC-based methods
[29,49,80,152]https://vision.cs.uwaterloo.ca/code
[79]https://GrabCut.weebly.com/code.html
[59]http://www.cs.cmu.edu/mohitg/segmentation.htm
[55]http://www.robots.ox.ac.uk/vgg/research/iseg/
[19]Code available upon request to author
[84]https://mmcheng.net/densecut/
[70]par http://coopcut.berkeleyvision.org/
[28]http://www1.icsi.berkeley.edu/cnieuwe/code/multisegpub.zip
[69]https://github.com/meng-tang/KernelCut
[71]https://github.com/aosokin/coopCuts_CVPR2013
[52,232]https://github.com/meng-tang/KernelCut_ICCV15/
[205]https://github.com/Borda/pyImSegm
Table 4 Available resources in the field of IIS
[134]   Liu, Y.; Yu, Y. Interactive image segmentation based on level sets of probabilities. IEEE Transactions on Visualization and Computer Graphics Vol. 18, No. 2, 202-213, 2012.
[135]   Scheuermann, B.; Rosenhahn, B. Interactive image segmentation using level sets and dempster-shafer theory of evidence. In: Image Analysis. Lecture Notes in Computer Science, Vol. 6688. Heyden, A.; Kahl, F. Eds. Springer Berlin Heidelberg, 656-665, 2011.
[1]   Zhu, H. Y.; Meng, F. M.; Cai, J. F.; Lu, S. J. Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation. Journal of Visual Communication and Image Representation Vol. 34, 12-27, 2016.
[2]   Jain, S.; Laxmi, V. Color image segmentation techniques: A survey. In: Proceedings of the International Conference on Microelectronics, Computing & Communication Systems. Lecture Notes in Electrical Engineering, Vol. 453. Nath, V. Ed. Springer Singapore, 189-197, 2017.
[3]   Yu, H. S.; Yang, Z. G.; Tan, L.; Wang, Y. N.; Sun, W.; Sun, M. G.; Tang, Y. D. Methods and datasets on semantic segmentation: A review. Neurocomputing Vol. 304, 82-103, 2018.
[4]   Suri, J. S.; Setarehdan, S. K.; Singh, S. Advanced Algorithmic Approaches to Medical Image Segmentation: State-of-the-Art Applications in Cardiology, Neurology, Mammography and Pathology. Springer-Verlag London, 2001.
[136]   Li, Y. P.; Cao, G.; Wang, T.; Cui, Q. J.; Wang, B. S. A novel local region-based active contour model for image segmentation using Bayes theorem. Information Sciences Vol. 506, 443-456, 2020.
[137]   Mylona, E. A.; Savelonas, M. A.; Maroulis, D. Automated parameterization of active contours: A brief survey. In: Proceedings of the IEEE International Symposium on Signal Processing and Information Technology, 344-349, 2013.
[5]   Chen, X. J.; Pan, L. J. A survey of graph cuts/graph search based medical image segmentation. IEEE Reviews in Biomedical Engineering Vol. 11, 112-124, 2018.
[6]   McGuinness, K.; O’Connor, N. E. A comparative evaluation of interactive segmentation algorithms. Pattern Recognition Vol. 43, No. 2, 434-444, 2010.
[138]   Boykov, Y.; Funka-Lea, G. Graph cuts and efficient N-D image segmentation. International Journal of Computer Vision Vol. 70, No. 2, 109-131, 2006.
[139]   Greig, D. M.; Porteous, B. T.; Seheult, A. H. Exact maximum a posteriori estimation for binary images. Journal of the Royal Statistical Society: Series B (Methodological) Vol. 51, No. 2, 271-279, 1989.
[7]   He, J.; Kim, C. S.; Kuo, C. C. J. Interactive image segmentation techniques. In: Interactive Segmentation Techniques. SpringerBriefs in Electrical and Computer Engineering. Springer Singapore, 17-62, 2013.
[8]   Xian, M.; Zhang, Y.; Cheng, H.-D.; Xu, F.; Ding, J. Neutro-connectedness cut. IEEE Transactions on Image Processing Vol. 25, No. 10, 4691-4703, 2016.
[140]   Boykov, Y.; Kolmogorov, V. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 26, No. 9, 1124-1137, 2004.
[141]   Vincent, L.; Soille, P. Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 13, No. 6, 583-598, 1991.
[142]   Blake, A.; Rother, C.; Brown, M.; Perez, P.; Torr, P. Interactive image segmentation using an adaptive GMMRF model. In: Computer Vision - ECCV 2004. ECCV 2004. Lecture Notes in Computer Science, Vol. 3021. Pajdla, T.; Matas, J. Eds. Springer Berlin Heidelberg, 428-441, 2004.
[143]   Besag, J. On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society: Series B (Methodological) Vol. 48, 259-279, 1986.
[144]   Lobacheva, E.; Veksler, O.; Boykov, Y. Joint optimization of segmentation and color clustering. In: Proceedings of the IEEE International Conference on Computer Vision, 1626-1634, 2015.
[145]   Hartigan, J. A.; Wong, M. A. Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics) Vol. 28, No. 1, 100-108, 1979.
[146]   Boykov, Y.; Veksler, O.; Zabih, R. Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 23, No. 11, 1222-1239, 2001.
[147]   Zhou, H. L.; Zheng, J. M.; Wei, L. Texture aware image segmentation using graph cuts and active contours. Pattern Recognition Vol. 46, No. 6, 1719-1733, 2013.
[148]   Wang, T.; Ji, Z. X.; Sun, Q. S.; Han, S. D. Combining pixel-level and patch-level information for segmentation. Neurocomputing Vol. 158, 13-25, 2015.
[149]   Criminisi, A.; Sharp, T.; Blake, A. GeoS: Geodesic image segmentation. In: Computer Vision-ECCV 2008. Lecture Notes in Computer Science, Vol. 5302. Forsyth, D.; Torr, P.; Zisserman, A. Eds. Springer Berlin Heidelberg, 99-112, 2008.
[9]   Chen, D. J.; Chen, H. T.; Chang, L. W. Interactive segmentation from 1-bit feedback. In: Computer Vision-ACCV 2016. Lecture Notes in Computer Science, Vol. 10111. Lai, S. H.; Lepetit, V.; Nishino, K.; Sato, Y. Eds. Springer Cham, 261-274, 2017.
[10]   Long, J.; Shelhamer, E.; Darrell, T. Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3431-3440, 2015.
[150]   Peng, Z. L.; Qu, S. J.; Li, Q. L. Interactive image segmentation using geodesic appearance overlap graph cut. Signal Processing: Image Communication Vol. 78, 159-170, 2019.
[151]   Veksler, O. Star shape prior for graph-cut image segmentation. In:Computer Vision-ECCV 2008. Lecture Notes in Computer Science, Vol. 5304. Forsyth, D.; Torr, P.; Zisserman, A. Eds. Springer Berlin Heidelberg, 454-467, 2008.
[152]   Gorelick, L.; Veksler, O.; Boykov, Y.; Nieuwenhuis, C. Convexity shape prior for segmentation. In: Computer Vision-ECCV 2014. Lecture Notes in Computer Science, Vol. 8693. Fleet, D.; Pajdla, T.; Schiele, B.; Tuytelaars, T. Eds. Springer Cham, 675-690, 2014.
[153]   Freedman, D.; Zhang, T. Interactive graph cut based segmentation with shape priors. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 755-762, 2005.
[11]   Yao, R.; Lin, G.; Xia, S.; Zhao, J.; Zhou, Y. Video object segmentation and tracking: A survey arXiv preprint arXiv:1904.09172, 2019.
[12]   Mortensen, E. N.; Barrett, W. A. Interactive segmentation with intelligent scissors. Graphical Models and Image Processing Vol. 60, No. 5, 349-384, 1998.
[154]   Das, P.; Veksler, O.; Zavadsky, V.; Boykov, Y. Semiautomatic segmentation with compact shape prior. Image and Vision Computing Vol. 27, Nos. 1-2, 206-219, 2009.
[155]   Zeng, Y.; Samaras, D.; Chen, W.; Peng, Q. S. Topology cuts: A novel min-cut/max-flow algorithm for topology preserving segmentation in N-D images. Computer Vision and Image Understanding Vol. 112, No. 1, 81-90, 2008.
[156]   Chen, L.; Cheng, H. D.; Zhang, J. P. Fuzzy subfiber and its application to seismic lithology classification. Information Sciences-Applications Vol. 1, No. 2, 77-95, 1994.
[157]   Ciesielski, K. C.; Miranda, P. A. V.; Falc?o, A. X.; Udupa, J. K. Joint graph cut and relative fuzzy connectedness image segmentation algorithm. Medical Image Analysis Vol. 17, No. 8, 1046-1057, 2013.
[158]   Xian, M.; Cheng, H. D.; Zhang, Y. A fully automatic breast ultrasound image segmentation approach based on neutro-connectedness. In: Proceedings of the 22nd International Conference on Pattern Recognition, 2495-2500, 2014.
[159]   He, K.; Wang, D.; Tong, M.; Zhang, X. Interactive image segmentation on multiscale appearances. IEEE Access Vol. 6, 67732-67741, 2018.
[160]   Kim, T. H.; Lee, K. M.; Lee, S. U. Generative image segmentation using random walks with restart. In: Computer Vision-ECCV 2008. Lecture Notes in Computer Science, Vol. 5304. Forsyth, D.; Torr, P.; Zisserman, A. Eds. Springer Berlin Heidelberg, 264-275, 2008.
[161]   Dong, X. P.; Shen, J. B.; Shao, L.; van Gool, L. Sub-Markov random walk for image segmentation. IEEE Transactions on Image Processing Vol. 25, No. 2, 516-527, 2016.
[162]   Bampis, C. G.; Maragos, P. Unifying the random walker algorithm and the SIR model for graph clustering and image segmentation. In: Proceedings of the IEEE International Conference on Image Processing, 2265-2269, 2015.
[163]   Ham, B.; Min, D. B.; Sohn, K. A generalized random walk with restart and its application in depth up-sampling and interactive segmentation. IEEE Transactions on Image Processing Vol. 22, No. 7, 2574-2588, 2013.
[164]   Shen, J.; Du, Y.; Li, X. Interactive segmentation using constrained laplacian optimization. IEEE Transactions on Circuits and Systems for Video Technology Vol. 24, No. 7, 1088-1100, 2014.
[165]   Xie, X.; Yu, Z.; Gu, Z.; Li, Y. An iterative boundary random walks algorithm for interactive image segmentation. arXiv preprint arXiv:1808.03002, 2018.
[166]   Sener, O.; Ugur, K.; Alatan, A. A. Error-tolerant interactive image segmentation using dynamic and iterated graph-cuts. In: Proceedings of the 2nd ACM International Workshop on Interactive Multimedia on Mobile and Portable Devices, 9-16, 2012.
[167]   Sinop, A. K.; Grady, L. A seeded image segmentation framework unifying graph cuts and random walker which yields a new algorithm. In: Proceedings of the IEEE 11th International Conference on Computer Vision, 1-8, 2007.
[168]   Mehnert, A.; Jackway, P. An improved seeded region growing algorithm. Pattern Recognition Letters Vol. 18, No. 10, 1065-1071, 1997.
[169]   Beare, R. Regularized seeded region growing. In: Proceedings of the 6th International Symposium on Mathematical Morphology, 91-99, 2002.
[170]   Fan, J. P.; Zeng, G. H.; Body, M.; Hacid, M. S. Seeded region growing: An extensive and comparative study. Pattern Recognition Letters Vol. 26, No. 8, 1139-1156, 2005.
[171]   Beare, R. A locally constrained watershed transform.IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 28, No. 7, 1063-1074, 2006.
[172]   Heimann, T.; Thorn, M.; Kunert, T.; Meinzer, H.-P. New methods for leak detection and contour correction in seeded region growing segmentation. In: Proceedings of the 20th ISPRS Congress Technical Commission V, 317-322, 2004.
[173]   Comaniciu, D.; Meer, P. Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 24, No. 5, 603-619, 2002.
[174]   Zhou, C.; Liu, C. Interactive image segmentation based on region merging using hierarchical match mechanism. In: Proceedings of the International Conference on Computer Science and Service System, 1781-1784, 2012.
[175]   Dong, R.; Wang, B.; Li, S.; Zhou, Z.; Li, S.; Wang, Z. Interactive image segmentation with color and texture information by region merging. In: Proceedings of the Chinese Control and Decision Conference, 777-783, 2016.
[176]   Minaee, S.; Boykov, Y.; Porikli, F.; Plaza, A.; Kehtarnavaz, N.; Terzopoulos, D. Image segmentation using deep learning: A survey. arXiv preprint arXiv:2001.05566, 2020.
[177]   He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770-778, 2016.
[178]   Boroujerdi, A. S.; Khanian, M.; Breu?, M. Deep interactive region segmentation and captioning. In: Proceedings of the 13th International Conference on Signal-Image Technology & Internet-Based Systems, 103-110, 2017.
[179]   Liew, J.; Wei, Y.; Xiong, W.; Ong, S.-H.; Feng, J. Regional interactive image segmentation networks. In: Proceedings of the IEEE International Conference on Computer Vision, 2746-2754, 2017.
[180]   Hu, Y.; Soltoggio, A.; Lock, R.; Carter, S. A fully convolutional two-stream fusion network for interactive image segmentation. Neural Networks Vol. 109, 31-42, 2019.
[181]   Majumder, S.; Yao, A. Content-aware multi-level guidance for interactive instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 11602-11611, 2019.
[182]   Castrejon, L.; Kundu, K.; Urtasun, R.; Fidler, S. Annotating object instances with a polygon-RNN. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5230-5238, 2017.
[183]   Acuna, D.; Ling, H.; Kar, A.; Fidler, S. Efficient interactive annotation of segmentation datasets with polygon-RNN++. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 859-868, 2018.
[184]   Ling, H.; Gao, J.; Kar, A.; Chen, W.; Fidler, S. Fast interactive object annotation with curve-GCN. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5257-5266, 2019.
[185]   Wang, Z.; Acuna, D.; Ling, H.; Kar, A.; Fidler, S. Object instance annotation with deep extreme level set evolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 7500-7508, 2019.
[186]   Sofiiuk, K.; Petrov, I.; Barinova, O.; Konushin, A. f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8623-8632, 2020.
[187]   He, K.; Gkioxari, G.; Dollár, P.; Girshick, R. Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, 2961-2969, 2017.
[188]   Liew, J. H.; Cohen, S.; Price, B.; Mai, L.; Ong, S.-H.; Feng, J. MultiSeg: Semantically meaningful, scale-diverse segmentations from minimal user input. In: Proceedings of the IEEE International Conference on Computer Vision, 662-670, 2019.
[189]   Lin, Z.; Zhang, Z.; Chen, L.-Z.; Cheng, M.-M.; Lu, S.-P. Interactive image segmentation with first click attention. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 13339-13348, 2020.
[190]   Wang, T.; Yao, Y.; Chen, Y.; Zhang, M.; Tao, F.; Snoussi, H. Auto-sorting system toward smart factory based on deep learning for image segmentation. IEEE Sensors Journal Vol. 18, No. 20, 8493-8501, 2018.
[191]   Noma, A.; Graciano, A. B. V.; Cesar, R. M.; Consularo, L. A.; Bloch, I. Interactive image segmentation by matching attributed relational graphs. Pattern Recognition Vol. 45, No. 3, 1159-1179, 2012.
[192]   Noma, A.; Pardo, A.; Cesar Jr., R. M. Structural matching of 2D electrophoresis gels using deformed graphs. Pattern Recognition Letters Vol. 32, No. 1, 3-11, 2011.
[193]   Jung, C.; Jian, M.; Liu, J.; Jiao, L. C.; Shen, Y. B. Interactive image segmentation via kernel propagation. Pattern Recognition Vol. 47, No. 8, 2745-2755, 2014.
[194]   Hu, E. L.; Chen, S. C.; Zhang, D. Q.; Yin, X. S. Semisupervised kernel matrix learning by kernel propagation. IEEE Transactions on Neural Networks Vol. 21, No. 11, 1831-1841, 2010.
[195]   Li, H.; Wu, W.; Wu, E. H. Robust interactive image segmentation via graph-based manifold ranking. Computational Visual Media Vol. 1, No. 3, 183-195, 2015.
[196]   Wang, B.; Tu, Z. Affinity learning via self-diffusion for image segmentation and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2312-2319, 2012.
[197]   Likas, A.; Vlassis, N.; Verbeek, J. J. The global k-means clustering algorithm. Pattern Recognition Vol. 36, No. 2, 451-461, 2003.
[198]   Belhumeur, P. N.; Hespanha, J. P.; Kriegman, D. J. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 19, No. 7, 711-720, 1997.
[199]   Zemene, E.; Pelillo, M. Interactive image segmentation using constrained dominant sets. In: Computer Vision-ECCV 2016. Lecture Notes in Computer Science, Vol. 9912. Leibe, B.; Matas, J.; Sebe, N.; Welling, M. Eds. Springer Cham, 278-294, 2016.
[200]   Bulò, S. R.; Pelillo, M. Dominant-set clustering: A review. European Journal of Operational Research Vol. 262, No. 1, 1-13, 2017.
[201]   Breve, F. Interactive image segmentation using label propagation through complex networks. Expert Systems With Applications Vol. 123, 18-33, 2019.
[13]   Falc?o, A. X.; Udupa, J. K.; Samarasekera, S.; Sharma, S.; Hirsch, B. E.; de A Lotufo, R. User-steered image segmentation paradigms: Live wire and live lane. Graphical Models and Image Processing Vol. 60, No. 4, 233-260, 1998.
[14]   Falcao, A. X.; Udupa, J. K.; Miyazawa, F. K. An ultra-fast user-steered image segmentation paradigm: Live wire on the fly. IEEE Transactions on Medical Imaging Vol. 19, No. 1, 55-62, 2000.
[15]   Miranda, P. A. V.; Falcao, A. X.; Spina, T. V. Riverbed: A novel user-steered image segmentation method based on optimum boundary tracking. IEEE Transactions on Image Processing Vol. 21, No. 6, 3042-3052, 2012.
[16]   Adams, R.; Bischof, L. Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 16, No. 6, 641-647, 1994.
[17]   Maninis, K.-K.; Caelles, S.; Pont-Tuset, J.; Van Gool, L. Deep extreme cut: From extreme points to object segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 616-625, 2018.
[18]   Vezhnevets, V.; Konouchine, V. GrowCut: Interactive multi-label ND image segmentation by cellular automata. In: Proceedings of Graphicon, 150-156, 2005.
[19]   Xian, M.; Xu, F.; Cheng, H. D.; Zhang, Y.; Ding, J. EISeg: Effective interactive segmentation. In: Proceedings of the 23rd International Conference on Pattern Recognition, 1982-1987, 2016.
[20]   Meena, S.; Palaniappan, K.; Seetharaman, G. User driven sparse point-based image segmentation. In: Proceedings of the IEEE International Conference on Image Processing, 844-848, 2016.
[21]   Song, G.; Myeong, H.; Lee, K. M. SeedNet: Automatic seed generation with deep reinforcement learning for robust interactive segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1760-1768, 2018.
[22]   Mahadevan, S.; Voigtlaender, P.; Leibe, B. Iteratively trained interactive segmentation. In: Proceedings of the British Machine Vision Conference, 212, 2018.
[23]   Xu, N.; Price, B.; Cohen, S.; Yang, J.; Huang, T. S. Deep interactive object selection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 373-381, 2016.
[24]   Li, Z.; Chen, Q.; Koltun, V. Interactive image segmentation with latent diversity. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 577-585, 2018.
[25]   Fan, M.; Lee, T. C. M. Variants of seeded region growing. IET Image Process Vol. 9, No. 6, 478-485, 2014.
[26]   Xu, J.; Collins, M. D.; Singh, V. Incorporating topological constraints within interactive segmentation and contour completion via discrete calculus. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013.
[27]   Friedland, G.; Jantz, K.; Rojas, R. Siox: Simple interactive object extraction in still images. In: Proceedings of the 7th IEEE International Symposium on Multimedia, 253-260, 2005.
[28]   Nieuwenhuis, C.; Cremers, D. Spatially varying color distributions for interactive multilabel segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 35, No. 5, 1234-1247, 2013.
[29]   Stuhmer, J.; Schroder, P.; Cremers, D. Tree shape priors with connectivity constraints using convex relaxation on general graphs. In: Proceedings of the IEEE International Conference on Computer Vision, 2336-2343, 2013.
[30]   Xiang, S. M.; Nie, F. P.; Zhang, C. X.; Zhang, C. S. Interactive natural image segmentation via spline regression. IEEE Transactions on Image Processing Vol. 18, No. 7, 1623-1632, 2009.
[31]   Long, J. W.; Feng, X.; Zhu, X. F.; Zhang, J. X.; Gou, G. L. Efficient superpixel-guided interactive image segmentation based on graph theory. Symmetry Vol. 10, No. 5, 169, 2018.
[32]   Duchenne, O.; Audibert, J.-Y.; Keriven, R.; Ponce, J.; Ségonne, F. Segmentation by transduction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2008.
[33]   Wang, T.; Yang, J.; Sun, Q.; Ji, Z.; Fu, P.; Ge, Q. Global graph diffusion for interactive object extraction. Information Sciences Vols. 460-461, 103-114, 2018.
[34]   Xiang, S. M.; Pan, C. H.; Nie, F. P.; Zhang, C. S. Interactive image segmentation with multiple linear reconstructions in windows. IEEE Transactions on Multimedia Vol. 13, No. 2, 342-352, 2011.
[35]   Meshry, M.; Taha, A.; Torki, M. Multi-modality feature transform: An interactive image segmentation approach. In: Proceedings of the British Machine Vision Conference, 2015.
[36]   Ren, Y.; Chua, C. S.; Ho, Y. K. Statistical background modeling for non-stationary camera. Pattern Recognition Letters Vol. 24, Nos. 1-3, 183-196, 2003.
[37]   Kim, T. H.; Lee, K. M.; Lee, S. U. Nonparametric higher-order learning for interactive segmentation. In: Proceedings of the Computer Vision and Pattern Recognition, 3201-3208, 2010.
[38]   Bai, J.; Wu, X. Error-tolerant scribbles based interactive image segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 392-399, 2014.
[39]   Zhang, J.; Tang, Z. H.; Gui, W. H.; Chen, Q.; Liu, J. P. Interactive image segmentation with a regression based ensemble learning paradigm. Frontiers of Information Technology & Electronic Engineering Vol. 18, No. 7, 1002-1020, 2017.
[40]   Subr, K.; Paris, S.; Soler, C.; Kautz, J. Accurate binary image selection from inaccurate user input. Computer Graphics Forum Vol. 32, No. 2pt1, 41-50, 2013.
[41]   Kohli P.; Ladicky, L.; Torr P. H. S. Robust higher order potentials for enforcing label consistency. International Journal of Computer Vision Vol. 82, 302-324, 2009.
[42]   Salembier, P.; Garrido, L. Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval. IEEE Transactions on Image Processing Vol. 9, No. 4, 561-576, 2000.
[43]   Jian, M.; Jung, C. Interactive image segmentation using adaptive constraint propagation. IEEE Transactions on Image Processing Vol. 25, No. 3, 1301-1311, 2016.
[44]   Li, W.; Shi, Y.; Yang, W.; Wang, H.; Gao, Y. Interactive image segmentation via cascaded metric learning. In: Proceedings of the IEEE International Conference on Image Processing, 2900-2904, 2015.
[45]   Cheng, D. S.; Murino, V.; Figueiredo, M. Clustering under prior knowledge with application to image segmentation. In: Proceedings of the 19th International Conference on Neural Information Processing Systems, 401-408, 2006.
[46]   Luo, L.; Wang, X.; Hu, S.; Hu, X.; Chen, L. Interactive image segmentation based on samples reconstruction and FLDA. Journal of Visual Communication and Image Representation Vol. 43, 138-151, 2017.
[47]   Mansilla, L. A. C.; Miranda, P. A. V. Oriented image foresting transform segmentation: Connectivity constraints with adjustable width. In: Proceedings of the 29th SIBGRAPI Conference on Graphics, Patterns and Images, 289-296, 2016.
[48]   Taha, A.; Torki, M. Seeded laplaican: An eigenfunction solution for scribble based interactive image segmentation. arXiv preprint arXiv:1702.00882, 2017.
[49]   Boykov, Y. Y.; Jolly, M.-P. Interactive graph cuts for optimal boundary & region segmentation of objects in ND images. In: Proceedings of the 8th IEEE International Conference on Computer Vision, Vol. 1, 105-112, 2001.
[50]   Grady, L. Random walks for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 11, 1768-1783, 2006.
[51]   Wang, T.; Ji, Z. X.; Sun, Q. S.; Chen, Q.; Han, S. D. Image segmentation based on weighting boundary information via graph cut. Journal of Visual Communication and Image Representation Vol. 33, 10-19, 2015.
[52]   Tang, M.; Marin, D.; Ayed, I. B.; Boykov, Y. Kernel cuts: MRF meets kernel & spectral clustering. arXiv preprint arXiv:1506.07439, 2015.
[53]   Bai, X.; Sapiro, G. A geodesic framework for fast interactive image and video segmentation and matting. In: Proceedings of the IEEE 11th International Conference on Computer Vision, 1-8, 2007.
[54]   Price, B. L.; Morse, B.; Cohen, S. Geodesic graph cut for interactive image segmentation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 3161-3168, 2010.
[55]   Gulshan, V.; Rother, C.; Criminisi, A.; Blake, A.; Zisserman, A. Geodesic star convexity for interactive image segmentation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 3129-3136, 2010.
[56]   Gong, Y.; Xiang, S.; Wang, L.; Pan, C. Fine-structured object segmentation via edge-guided graph cut with interaction simplification. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 1801-1805, 2016.
[57]   Vicente, S.; Kolmogorov, V.; Rother, C. Graph cut based image segmentation with connectivity priors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2008.
[58]   Casaca, W.; Nonato, L. G.; Taubin, G. Laplacian coordinates for seeded image segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 384-391, 2014.
[59]   Li, Y.; Sun, J.; Tang, C.-K.; Shum, H.-Y. Lazy snapping. ACM Transactions on Graphics Vol. 23, No. 3, 303-308, 2004.
[60]   Sung, M.-C.; Chang, L.-W. Using multi-layer random walker for image segmentation. In: Procedings of the International Workshop on Advanced Image Technology, 1-4, 2018.
[202]   Wang, T.; Sun, Q. S.; Ji, Z. X.; Chen, Q.; Fu, P. Multi-layer graph constraints for interactive image segmentation via game theory. Pattern Recognition Vol. 55, 28-44, 2016.
[203]   Achanta, R.; Shaji, A.; Smith, K.; Lucchi, A.; Fua, P.; Süsstrunk, S. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 34, No. 11, 2274-2282, 2012.
[61]   Wang, J. Discriminative Gaussian mixtures for interactive image segmentation. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 601-604, 2007.
[62]   Yang, W. X.; Cai, J. F.; Zheng, J. M.; Luo, J. B. User-friendly interactive image segmentation through unified combinatorial user inputs. IEEE Transactions on Image Processing Vol. 19, No. 9, 2470-2479, 2010.
[63]   Shi, R.; Liu, Z.; Xue, Y.; Zhang, X. Interactive object segmentation using iterative adjustable graph cut. In: Proceedings of the Visual Communications and Image Processing, 1-4, 2011.
[64]   Wang, T.; Ji, Z. X.; Sun, Q. S.; Chen, Q.; Ge, Q.; Yang, J. Diffusive likelihood for interactive image segmentation. Pattern Recognition Vol. 79, 440-451, 2018.
[65]   Peng, B.; Zhang, L.; Zhang, D.; Yang, J. Image segmentation by iterated region merging with localized graph cuts. Pattern Recognition Vol. 44, Nos. 10-11, 2527-2538, 2011.
[66]   Bampis, C. G.; Maragos, P.; Bovik, A. C. Graph-driven diffusion and random walk schemes for image segmentation. IEEE Transactions on Image Processing Vol. 26, No. 1, 35-50, 2017.
[67]   Zhang, J.; Zheng, J.; Cai, J. A diffusion approach to seeded image segmentation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2125-2132, 2010.
[68]   Ducournau, A.; Bretto, A. Random walks in directed hypergraphs and application to semi-supervised image segmentation. Computer Vision and Image Understanding Vol. 120, 91-102, 2014.
[69]   Tang, M.; Marin, D.; Ayed, I. B.; Boykov, Y. Normalized cut meets MRF. In: Computer Vision-ECCV 2016. Lecture Notes in Computer Science, Vol. 9906. Leibe, B.; Matas, J.; Sebe, N.; Welling, M. Eds. Springer Cham, 748-765, 2016.
[70]   Jegelka, S.; Bilmes, J. Submodularity beyond submodular energies: Coupling edges in graph cuts. In: Proceedings of the Computer Vision and Pattern Recognition, 1897-1904, 2011.
[71]   Kohli, P.; Osokin, A.; Jegelka, S. A principled deep random field model for image segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1971-1978, 2013.
[72]   Nguyen, T. N. A.; Cai, J.; Zhang, J.; Zheng, J. Robust interactive image segmentation using convex active contours. IEEE Transactions on Image Processing Vol. 21, No. 8, 3734-3743, 2012.
[73]   Liu, D.; Xiong, Y.; Shapiro, L.; Pulli, K. Robust interactive image segmentation with automatic boundary refinement. In: Proceedings of the 17th IEEE International Conference on Image Processing, 225-228, 2010.
[74]   Li, H.; Gong, M.; Miao, Q.; Wang, B. Interactive active contour with kernel descriptor. Information Sciences Vol. 450, 53-72, 2018.
[75]   Ning, J. F.; Zhang, L.; Zhang, D.; Wu, C. K. Interactive image segmentation by maximal similarity based region merging. Pattern Recognition Vol. 43, No. 2, 445-456, 2010.
[76]   Zhou, C. B.; Wu, D. M.; Qin, W. H.; Liu, C. C. An efficient two-stage region merging method for interactive image segmentation. Computers & Electrical Engineering Vol. 54, 220-229, 2016.
[204]   Mathieu, B.; Crouzil, A.; Puel, J. B. Interactive segmentation: A scalable superpixel-based method. Journal of Electronic Imaging Vol. 26, No. 6, 061606, 2017.
[205]   Borovec, J.; ?vihlík, J.; Kybic, J.; Habart, D. Supervised and unsupervised segmentation using superpixels, model estimation, and graph cut. Journal of Electronic Imaging Vol. 26, No. 6, 061610, 2017.
[206]   Borovec, J.; Kybic, J.; Sugimoto, A. Region growing using superpixels with learned shape prior. Journal of Electronic Imaging Vol. 26, No. 6, 061611, 2017.
[207]   Zhou, Y.; Ju, L.; Wang, S. Multiscale superpixels and supervoxels based on hierarchical edge-weighted centroidal voronoi tessellation.IEEE Transactions on Image Processing Vol. 24, No. 11, 3834-3845, 2015.
[77]   Vallin Spina, T.; de Miranda, P. A. V.; Xavier Falcao, A. Hybrid approaches for interactive image segmentation using the live markers paradigm. IEEE Transactions on Image Processing Vol. 23, No. 12, 5756-5769, 2014.
[78]   Falcao, A. X.; Stolfi, J.; de Alencar Lotufo, R. The image foresting transform: Theory, algorithms, and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 26, No. 1, 19-29, 2004.
[79]   Rother, C.; Kolmogorov, V.; Blake, A. Grabcut: Interactive foreground extraction using iterated graph cuts. ACM Transactions on Graphics Vol. 23, No. 23, 309-314, 2004.
[80]   Tang, M.; Gorelick, L.; Veksler, O.; Boykov, Y. Grabcut in one cut. In: Proceedings of the IEEE International Conference on Computer Vision, 1769-1776, 2013.
[81]   Yu, H.; Zhou, Y.; Qian, H.; Xian, M.; Wang, S. Loosecut: Interactive image segmentation with loosely bounded boxes. In: Proceedings of the IEEE International Conference on Image Processing, 3335-3339, 2017.
[82]   Oh, C.; Ham, B.; Sohn, K. Point-cut: Interactive image segmentation using point supervision. In: Computer Vision-ACCV 2016. Lecture Notes in Computer Science, Vol. 10111. Lai, S. H.; Lepetit, V.; Nishino, K.; Sato, Y. Eds. Springer Cham, 229-244, 2017.
[83]   Wu, S. Q.; Nakao, M.; Matsuda, T. SuperCut: Superpixel based foreground extraction with loose bounding boxes in one cutting. IEEE Signal Processing Letters Vol. 24, No. 12, 1803-1807, 2017.
[84]   Cheng, M. M.; Prisacariu, V. A.; Zheng, S.; Torr, P. H. S.; Rother, C. DenseCut: Densely connected CRFs for realtime GrabCut. Computer Graphics Forum Vol. 34, No. 7, 193-201, 2015.
[208]   Arbeláez, P.; Maire, M.; Fowlkes, C.; Malik, J. Contour detection and hierarchical image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 33, No. 5, 898-916, 2011.
[209]   Luo, L. K.; Wang, X. F.; Hu, S. Q.; Hu, X.; Zhang, H. L.; Liu, Y. H.; Zhang, J. A unified framework for interactive image segmentation via Fisher rules. The Visual Computer Vol. 35, No. 12, 1869-1882, 2018.
[210]   Wang, X. F.; Tang, Y. X.; Masnou, S.; Chen, L. M. A global/local affinity graph for image segmentation. IEEE Transactions on Image Processing Vol. 24, No. 4, 1399-1411, 2015.
[211]   Shi, R.; Ngan, K. N.; Li, S. N.; Li, H. L. Interactive object segmentation in two phases. Signal Processing: Image Communication Vol. 65, 107-114, 2018.
[212]   Wang, T.; Ji, Z. X.; Sun, Q. S.; Chen, Q.; Jing, X. Y. Interactive multilabel image segmentation via robust multilayer graph constraints. IEEE Transactions on Multimedia Vol. 18, No. 12, 2358-2371, 2016.
[213]   Van den Bergh, M.; Boix, X.; Roig, G.; de Capitani, B.; van Gool, L. SEEDS: Superpixels extracted via energy-driven sampling. In: Computer Vision-ECCV 2012. Lecture Notes in Computer Science, Vol. 7578. Fitzgibbon, A.; Lazebnik, S.; Perona, P.; Sato, Y.; Schmid, C. Eds. Springer Berlin Heidelberg, 13-26, 2012.
[214]   Stutz, D.; Hermans, A.; Leibe, B. Superpixels: An evaluation of the state-of-the-art. Computer Vision and Image Understanding Vol. 166, 1-27, 2018.
[215]   Martin, D.; Fowlkes, C.; Tal, D.; Malik, J. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of the 8th IEEE International Conference on Computer Vision, 416-423, 2010.
[216]   Cheng, M.-M.; Mitra, N. J.; Huang, X.; Torr, P. H. S.; Hu, S.-M. Global contrast based salient region detection. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 37, No. 3, 569-582, 2015.
[217]   Everingham, M.; Eslami, S. M. A.; van Gool, L.; Williams, C. K. I.; Winn, J.; Zisserman, A. The pascal visual object classes challenge: A retrospective. International Journal of Computer Vision Vol. 111, No. 1, 98-136, 2015.
[218]   Rhemann, C.; Rother, C.; Wang, J.; Gelautz, M.; Kohli, P.; Rott, P. A perceptually motivated online benchmark for image matting. In: Proceedings of theIEEE Conference on Computer Vision and Pattern Recognition, 1826-1833, 2009.
[219]   Alpert, S.; Galun, M.; Brandt, A.; Basri, R. Image segmentation by probabilistic bottom-up aggregation and cue integration. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 34, No. 2, 315-327, 2012.
[220]   Lin, T. Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; Dollár, P.; Zitnick, C. L. Microsoft COCO: Common objects in context. In: Computer Vision-ECCV 2014. Lecture Notes in Computer Science, Vol. 8693. Fleet, D.; Pajdla, T.; Schiele, B.; Tuytelaars, T. Eds. Springer Cham, 740-755, 2014.
[221]   Cordts, M.; Omran, M.; Ramos, S.; Rehfeld, T.; Enzweiler, M.; Benenson, R.; Franke, U.; Roth, S.; Schiele, B. The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3213-3223, 2016.
[222]   Geiger, A.; Lenz, P.; Stiller, C.; Urtasun, R. Vision meets robotics: The KITTI dataset. The International Journal of Robotics Research Vol. 32, No. 11, 1231-1237, 2013.
[223]   Chen, L.-C.; Fidler, S.; Yuille, A. L.; Urtasun, R. Beat the mturkers: Automatic image labeling from weak 3D supervision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3198-3205, 2014.
[224]   Unnikrishnan, R.; Pantofaru, C.; Hebert, M. Toward objective evaluation of image segmentation algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 29, No. 6, 929-944, 2007.
[225]   Freixenet, J.; Mu?oz, X.; Raba, D.; Martí, J.; Cufí, X. Yet another survey on image segmentation: Region and boundary information integration. In: Computer Vision-ECCV 2002. Lecture Notes in Computer Science, Vol. 2352. Heyden, A.; Sparr, G.; Nielsen, M.; Johansen, P. Eds. Springer Berlin Heidelberg, 408-422, 2002.
[226]   Meila, M. Comparing clusterings: An axiomatic view. In: Proceedings of the 22nd International Conference on Machine Learning, 577-584, 2005.
[227]   Dubuisson, M.-P.; Jain, A. K. A modified Hausdorff distance for object matching. In: Proceedings of the 12th International Conference on Pattern Recognition, 566-568, 1994.
[228]   Perazzi, F.; Pont-Tuset, J.; McWilliams, B.; van Gool, L.; Gross, M.; Sorkine-Hornung, A. A benchmark dataset and evaluation methodology for video object segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 724-732, 2016.
[229]   Peng, B.; Zhang, L.; Zhang, D. A survey of graph theoretical approaches to image segmentation. Pattern Recognition Vol. 46, No. 3, 1020-1038, 2013.
[230]   Shen, J.; Du, Y.; Wang, W.; Li, X. Lazy random walks for superpixel segmentation. IEEE Transactions on Image Processing Vol. 23, No. 4, 1451-1462, 2014.
[231]   Wang, T.; Yang, J.; Ji, Z. X.; Sun, Q. S. Probabilistic diffusion for interactive image segmentation. IEEE Transactions on Image Processing Vol. 28, No. 1, 330-342, 2019.
[232]   Tang, M.; Ben Ayed, I.; Marin, D.; Boykov, Y. Secrets of grabcut and kernel k-means. In: Proceedings of the IEEE International Conference on Computer Vision, 1555-1563, 2015.
[85]   Rajchl, M.; Lee, M. C. H.; Oktay, O.; Kamnitsas, K.; Passerat-Palmbach, J.; Bai, W.; Damodaram, M.; Rutherford, M. A.; Hajnal, J. V.; Kainz, B. et al. Deepcut: Object segmentation from bounding box annotations using convolutional neural networks. IEEE Transactions on Medical Imaging Vol. 36, No. 2, 674-683, 2017.
[86]   Xu, N.; Price, B.; Cohen, S.; Yang, J.; Huang, T. Deep grabcut for object selection. In: Proeedings of the 28th British Machine Vision Conference, 2017.
[87]   Chen, Y. S.; Chan, A. B.; Wang, G. Adaptive figure-ground classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 654-661, 2012.
[88]   Chen, Y. S.; Chan, A. B. Enhanced figure-ground classification with background prior propagation. IEEE Transactions on Image Processing Vol. 24, No. 3, 873-885, 2015.
[89]   Lempitsky, V. S.; Kohli, P.; Rother, C.; Sharp, T. Image segmentation with a bounding box prior. In: Proceedings of the IEEE 12th International Conference on Computer Vision, 277-284, 2009.
[90]   Wu, J.; Zhao, Y.; Zhu, J.-Y.; Luo, S.; Tu, Z. Milcut: A sweeping line multiple instance learning paradigm for interactive image segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 256-263, 2014.
[91]   Kolotouros, N., Maragos, P. A finite element computational framework for active contours on graphs. arXiv preprint arXiv:171004346, 2017.
[92]   Choi, J.; Choi, J. Y. User interactive segmentation with partially growing random forest. In: Proceedings of the IEEE International Conference on Image Processing, 1090-1094, 2015.
[93]   Dai, L.; Ding, J.; Yang, J.; Zhang, F.; Li, J. Object extraction from bounding box prior with double sparse reconstruction. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, 903-911, 2015.
[94]   Tang, M.; Ayed, I. B; Boykov, Y. Pseudo-bound optimization for binary energies. In: Computer Vision-ECCV 2014. Lecture Notes in Computer Science, Vol. 8693. Fleet, D.; Pajdla, T.; Schiele, B.; Tuytelaars, T. Eds. Springer Cham, 691-707, 2014.
[95]   Gorelick, L.; Schmidt, F. R.; Boykov, Y. Fast trust region for segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1714-1721, 2013.
[96]   Ayed, I. B.; Gorelick, L.; Boykov, Y. Auxiliary cuts for general classes of higher order functionals. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1304-1311, 2013.
[97]   Li, K. Q.; Tao, W. B. Adaptive optimal shape prior for easy interactive object segmentation. IEEE Transactions on Multimedia Vol. 17, No. 7, 994-1005, 2015.
[98]   Liu, D. D.; Pulli, K.; Shapiro, L. G.; Xiong, Y. G. Fast interactive image segmentation by discriminative clustering. In: Proceedings of the ACM Multimedia Workshop on Mobile Cloud Media Computing, 47-52, 2010.
[99]   Zemene, E.; Alemu, L. T.; Pelillo, M. Dominant sets for ”constrained” image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 41, No. 10, 2438-2451, 2019.
[100]   Oh, C.; Ham, B.; Sohn, K. Robust interactive image segmentation using structure-aware labeling. Expert Systems With Applications Vol. 79, 90-100, 2017.
[101]   Hernández-Vela, A.; Primo, C.; Escalera, S. Automatic user interaction correction via Multi-label Graph cuts. In: Proceedings of the IEEE International Conference on Computer Vision, 1276-1281, 2011.
[102]   Wang, T. H.; Han, B.; Collomosse, J. TouchCut: Fast image and video segmentation using single-touch interaction. Computer Vision and Image Understanding Vol. 120, 14-30, 2014.
[103]   Jain, S. D.; Grauman, K. Click carving: Interactive object segmentation in images and videos with point clicks. International Journal of Computer Vision Vol. 127, No. 9, 1321-1344, 2019.
[104]   Chen, D.-J.; Chen, H.-T.; Chang, L.-W. Toward a unified scheme for fast interactive segmentation.Journal of Visual Communication and Image Representation Vol. 55, 393-403, 2018.
[105]   Benenson, R.; Popov, S.; Ferrari, V. Large-scale interactive object segmentation with human annotators. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 11700-11709, 2019.
[106]   Jang, W.-D.; Kim, C.-S. Interactive image segmentation via backpropagating refinement scheme. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 5297-5306, 2019.
[107]   Agustsson, E.; Uijlings, J. R. R.; Ferrari, V. Interactive full image segmentation by considering all regions jointly. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 11622-11631, 2019.
[108]   Cerrone, L.; Zeilmann, A.; Hamprecht, F. A. End-to-end learned random walker for seeded image segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 12559-12568, 2019.
[109]   Zheng, H. Y.; Chen, Y. F.; Yue, X. D.; Ma, C. Deep interactive segmentation of uncertain regions with shadowed sets. In: Proceedings of the 3rd International Symposium on Image Computing and Digital Medicine, 244-248, 2019.
[110]   Straehle, C.; Koethe, U.; Knott, G.; Briggman, K.; Denk, W.; Hamprecht, F. A. Seeded watershed cut uncertainty estimators for guided interactive segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Providence, 765-772, 2012.
[111]   Couprie, C.; Grady, L.; Najman, L.; Talbot, H. Power watershed: A unifying graph-based optimization framework. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 33, No. 7, 1384-1399, 2011.
[112]   Rupprecht, C.; Peter, L.; Navab, N. Image segmentation in twenty questions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3314-3322, 2015.
[113]   Chen, D. J.; Chen, H. T.; Chang, L. W. Interactive 1-bit feedback segmentation using transductive inference. Machine Vision and Applications Vol. 29, No. 4, 617-631, 2018.
[114]   Sourati, J.; Erdogmus, D.; Dy, J. G.; Brooks, D. H. Accelerated learning-based interactive image segmentation using pairwise constraints. IEEE Transactions on Image Processing Vol. 23, No. 7, 3057-3070, 2014.
[115]   Batra, D.; Kowdle, A.; Parikh, D.; Luo, J.; Chen, T. iCoseg: Interactive co-segmentation with intelligent scribble guidance. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 3169-3176, 2010.
[116]   Fathi, A.; Balcan, M. F.; Ren, X.; Rehg, J. M. Combining self training and active learning for video segmentation. In: Proceedings of the British Machine Vision Conference, 2011.
[117]   Kowdle, A.; Chang, Y.-J., Gallagher, A.; Chen, T. Active learning for piecewise planar 3D reconstruction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 929-936, 2011.
[118]   Gimp, G. N. U. Image manipulation program. User Manual. Edge-Detect Filters, Sobel, The GIMP Documentation Team, 8(2), 8-7, 2008.
[119]   Bresson, X.; Esedo?lu, S.; Vandergheynst, P.; Thiran, J. P.; Osher, S. Fast global minimization of the active contour/snake model. Journal of Mathematical Imaging and Vision Vol. 28, No. 2, 151-167, 2007.
[120]   Goldstein, T.; Bresson, X.; Osher, S. Geometric applications of the Split Bregman method: Segmentation and surface reconstruction. Journal of Scientific Computing Vol. 45, Nos. 1-3, 272-293, 2010.
[121]   Peng, Y.; Zhang, J.; Yuan, Y.; Zhu, S.; Fang, L. Robust interactive image segmentation via iterative refinement. In: Proceedings of the IEEE International Conference on Image Processing, 4383-4387, 2014.
[122]   Ali, H.; Rada, L.; Badshah, N. Image segmentation for intensity inhomogeneity in presence of high noise. IEEE Transactions on Image Processing Vol. 27, No. 8, 3729-3738, 2018.
[123]   Badshah, N.; Chen, K. Image selective segmentation under geometrical constraints using an active contour approach. Communications in Computational Physics Vol. 7, No. 4, 759-778, 2010.
[124]   Chan, T. F.; Vese, L. A. Active contours without edges. IEEE Transactions on Image Processing Vol. 10, No. 2, 266-277, 2001.
[125]   Gout, C.; Le Guyader, C.; Vese, L. Segmentation under geometrical conditions using geodesic active contours and interpolation using level set methods. Numerical Algorithms Vol. 39, Nos. 1-3, 155-173, 2005.
[126]   Abdelsamea, M. M.; Gnecco, G.; Gaber, M. M. An efficient Self-Organizing Active Contour model for image segmentation. Neurocomputing Vol. 149, 820-835, 2015.
[127]   Cremers, D.; Osher, S. J.; Soatto, S. Kernel density estimation and intrinsic alignment for knowledge-driven segmentation: Teaching level sets to walk. In: Pattern Recognition. Lecture Notes in Computer Science, Vol. 3175. Rasmussen, C. E.; Bülthoff, H. H.; Sch?lkopf, B.; Giese, M. A. Eds. Springer Berlin Heidelberg, 36-44, 2004.
[128]   Lee, C. P.; Snyder, W.; Wang, C. Supervised multispectral image segmentation using active contours. In: Proceedings of the IEEE International Conference on Robotics and Automation, 4242-4247, 2005.
[129]   Mille, J.; Bougleux, S.; Cohen, L. D. Combination of paths for interactive segmentation. In: Proceedings of the British Machine Vision Conference, 133.1-133.11, 2013.
[130]   Mille, J.; Bougleux, S.; Cohen, L. D. Combination of piecewise-geodesic paths for interactive segmentation. International Journal of Computer Vision Vol. 112, No. 1, 1-22, 2015.
[131]   Chen, D.; Mirebeau, J.-M.; Cohen, L. D. A new finsler minimal path model with curvature penalization for image segmentation and closed contour detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 355-363, 2016.
[132]   Chen, D.; Mirebeau, J.-M.; Cohen, L. D. Finsler geodesics evolution model for region based active contours. In: Proceedings of the British Machine Vision Conference, 22.1-22.12, 2016.
[133]   Chen, D.; Mirebeau, J. M.; Cohen, L. D. Global minimum for a finsler elastica minimal path approach. International Journal of Computer Vision Vol. 122, No. 3, 458-483, 2017.
[1] Weiheng Lin, Beibei Wang, Lu Wang, Nicolas Holzschuch. A detail preserving neural network model for Monte Carlo denoising[J]. Computational Visual Media, 2020, 6(2): 157-168.
[2] Xian Wu, Xiao-Nan Fang, Tao Chen, Fang-Lue Zhang. JMNet: A joint matting network for automatic human matting[J]. Computational Visual Media, 2020, 6(2): 215-224.
[3] Miao Wang, Xu-Quan Lyu, Yi-Jun Li, Fang-Lue Zhang. VR content creation and exploration with deep learning: A survey[J]. Computational Visual Media, 2020, 6(1): 3-28.
[4] Kin-Ming Wong, Tien-Tsin Wong. Deep residual learning for denoising Monte Carlo renderings[J]. Computational Visual Media, 2019, 5(3): 239-255.
[5] Shuyang Zhang, Runze Liang, Miao Wang. ShadowGAN: Shadow synthesis for virtual objects with conditional adversarial networks[J]. Computational Visual Media, 2019, 5(1): 105-115.
[6] Ruochen Fan, Xuanrun Wang, Qibin Hou, Hanchao Liu, Tai-Jiang Mu. SpinNet: Spinning convolutional network for lane boundary detection[J]. Computational Visual Media, 2019, 05(04): 417-428.
[7] Shuai Liu, Ruipeng Gang, Chenghua Li, Ruixia Song. Adaptive deep residual network for single image super-resolution[J]. Computational Visual Media, 2019, 05(04): 391-401.
[8] Jiahui Huang, Jun Gao, Vignesh Ganapathi-Subramanian, Hao Su, Yin Liu, Chengcheng Tang, Leonidas J. Guibas. DeepPrimitive: Image decomposition by layered primitive detection[J]. Computational Visual Media, 2018, 4(4): 385-397.
[9] Yoshikatsu Nakajima,Hideo Saito. Robust camera pose estimation by viewpoint classification using deep learning[J]. Computational Visual Media, 2017, 3(2): 189-198.
[10] Lin Chen,Meng Yang. Semi-supervised dictionary learning with label propagation for image classification[J]. Computational Visual Media, 2017, 3(1): 83-94.
[11] Hong Li,Wen Wu,Enhua Wu. Robust interactive image segmentation via graph-based manifold ranking[J]. Computational Visual Media, 2015, 1(3): 183-195.