Article

Open access

"GrabCut": interactive foreground extraction using iterated graph cuts

Authors:

Carsten Rother,

Vladimir Kolmogorov,

Andrew BlakeAuthors Info & Claims

SIGGRAPH '04: ACM SIGGRAPH 2004 Papers

Pages 309 - 314

https://doi.org/10.1145/1186562.1015720

Published: 01 August 2004 Publication History

Abstract

The problem of efficient, interactive foreground/background segmentation in still images is of great practical importance in image editing. Classical image segmentation tools use either texture (colour) information, e.g. Magic Wand, or edge (contrast) information, e.g. Intelligent Scissors. Recently, an approach based on optimization by graph-cut has been developed which successfully combines both types of information. In this paper we extend the graph-cut approach in three respects. First, we have developed a more powerful, iterative version of the optimisation. Secondly, the power of the iterative algorithm is used to simplify substantially the user interaction needed for a given quality of result. Thirdly, a robust algorithm for "border matting" has been developed to estimate simultaneously the alpha-matte around an object boundary and the colours of foreground pixels. We show that for moderately difficult examples the proposed method outperforms competitive tools.

Supplemental Material

MOV File

Download
11.47 KB

References

[1]

ADOBE SYSTEMS INCORP. 2002. Adobe Photoshop User Guide.

[2]

BLAKE, A., ROTHER, C., BROWN, M., PEREZ, P., AND TORR, P. 2004. Interactive Image Segmentation using an adaptive GMMRF model. In Proc. European Conf. Computer Vision.

[3]

BOYKOV, Y., AND JOLLY, M.-P. 2001. Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. In Proc. IEEE Int. Conf. on Computer Vision, CD--ROM.

[4]

BOYKOV, Y., AND KOLMOGOROV, V. 2003. Computing Geodesics and Minimal Surfaces via Graph Cut. In Proc. IEEE Int. Conf. on Computer Vision.

Digital Library

[5]

CASELLES, V., KIMMEL, R., AND SAPIRO, G. 1995. Geodesic active contours. In Proc. IEEE Int. Conf. on Computer Vision.

Digital Library

[6]

CHUANG, Y.-Y., CURLESS, B., SALESIN, D., AND SZELISKI, R. 2001. A Bayesian approach to digital matting. In Proc. IEEE Conf. Computer Vision and Pattern Recog., CD--ROM.

[7]

COREL CORPORATION. 2002. Knockout user guide.

[8]

DEMPSTER, A., LAIRD, M., AND RUBIN, D. 1977. Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. B. 39, 1--38.

[9]

GREIG, D., PORTEOUS, B., AND SEHEULT, A. 1989. Exact MAP estimation for binary images. J. Roy. Stat. Soc. B. 51, 271--279.

[10]

KASS, M., WITKIN, A., AND TERZOPOULOS, D. 1987. Snakes: Active contour models. In Proc. IEEE Int. Conf. on Computer Vision, 259--268.

[11]

KOLMOGOROV, V., AND ZABIH, R. 2002. What energy functions can be minimized via graph cuts? In Proc. ECCV. CD-ROM.

Digital Library

[12]

KWATRA, V., SCHÖDL, A., ESSA, I., TURK, G., AND BOBICK, A. 2003. Graphcut Textures: Image and Video Synthesis Using Graph Cuts. Proc. ACM Siggraph, 277--286.

Digital Library

[13]

MORTENSEN, E., AND BARRETT, W. 1995. Intelligent scissors for image composition. Proc. ACM Siggraph, 191--198.

Digital Library

[14]

MORTENSEN, E., AND BARRETT, W. 1999. Tobogan-based intelligent scissors with a four parameter edge model. In Proc. IEEE Conf. Computer Vision and Pattern Recog., vol. 2, 452--458.

[15]

RUCKLIDGE, W. J. 1996. Efficient visual recognition using the Hausdorff distance. LNCS. Springer-Verlag, NY.

Digital Library

[16]

RUZON, M., AND TOMASI, C. 2000. Alpha estimation in natural images. In Proc. IEEE Conf. Comp. Vision and Pattern Recog.

Cited By

Roby MRestrepo JPark HMuluk SEskandari MBaek SFinol E(2025)Automatic Segmentation of Abdominal Aortic Aneurysm From Computed Tomography Angiography Using a Patch-Based Dilated UNet ModelIEEE Access10.1109/ACCESS.2025.353341713(24544-24554)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3533417
Lang IXu FDecatur DBabu SHanocka R(2024)iSeg: Interactive 3D Segmentation via Interactive AttentionSIGGRAPH Asia 2024 Conference Papers10.1145/3680528.3687605(1-11)Online publication date: 3-Dec-2024
https://dl.acm.org/doi/10.1145/3680528.3687605
Guo XLi QMorrison-Smith SAnthony LZare ASong Y(2024)Elicitating Challenges and User Needs Associated with Annotation Software for Plant PhenotypingProceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640543.3645178(431-443)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3640543.3645178
Show More Cited By

Index Terms

"GrabCut": interactive foreground extraction using iterated graph cuts
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Interest point and salient region detections
  2. Computer graphics
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction devices
      1. Graphics input devices

Recommendations

"GrabCut": interactive foreground extraction using iterated graph cuts

The problem of efficient, interactive foreground/background segmentation in still images is of great practical importance in image editing. Classical image segmentation tools use either texture (colour) information, e.g. Magic Wand, or edge (contrast) ...
GrabCut: Interactive Foreground Extraction Using Iterated Graph Cuts
Seminal Graphics Papers: Pushing the Boundaries, Volume 2

The problem of efficient, interactive foreground/background segmentation in still images is of great practical importance in image editing. Classical image segmentation tools use either texture (colour) information, e.g. Magic Wand, or edge (contrast) ...
Image matting through a Web browser

Image matting is a process of foreground extraction from an image. An interactive, Web-based tool, called NIM 2.0, for image matting is presented in this paper. NIM is the first image matting tool accessible through a Web browser. Its algorithm has been ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGGRAPH '04: ACM SIGGRAPH 2004 Papers

August 2004

684 pages

ISBN:9781450378239

DOI:10.1145/1186562

Editor:
Joe Marks
Mitsubishi Electric Research Laboratories (MERL)

Copyright © 2004 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 August 2004

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Badges

Seminal Paper

Author Tags

Qualifiers

Article

Conference

SIGGRAPH04

Sponsor:

SIGGRAPH

SIGGRAPH04: Special Interest Group on Computer Graphics and Interactive Techniques

August 8 - 12, 2004

California, Los Angeles

Acceptance Rates

SIGGRAPH '04 Paper Acceptance Rate 83 of 478 submissions, 17%;

Overall Acceptance Rate 1,822 of 8,601 submissions, 21%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

967
Total Citations
View Citations
16,184
Total Downloads

Downloads (Last 12 months)609
Downloads (Last 6 weeks)68

Reflects downloads up to 08 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Roby MRestrepo JPark HMuluk SEskandari MBaek SFinol E(2025)Automatic Segmentation of Abdominal Aortic Aneurysm From Computed Tomography Angiography Using a Patch-Based Dilated UNet ModelIEEE Access10.1109/ACCESS.2025.353341713(24544-24554)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3533417
Lang IXu FDecatur DBabu SHanocka R(2024)iSeg: Interactive 3D Segmentation via Interactive AttentionSIGGRAPH Asia 2024 Conference Papers10.1145/3680528.3687605(1-11)Online publication date: 3-Dec-2024
https://dl.acm.org/doi/10.1145/3680528.3687605
Guo XLi QMorrison-Smith SAnthony LZare ASong Y(2024)Elicitating Challenges and User Needs Associated with Annotation Software for Plant PhenotypingProceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640543.3645178(431-443)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3640543.3645178
Ahmed YMittal A(2024)Unsupervised Co-generation of Foreground-Background Segmentation from Text-to-Image Synthesis2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV57701.2024.00498(5046-5057)Online publication date: 3-Jan-2024
https://doi.org/10.1109/WACV57701.2024.00498
Venjarski JLikó LTibenský ŠVančo MRozinaj G(2024)Keypoint-Based Foreground-Background Image Segmentation2024 International Symposium ELMAR10.1109/ELMAR62909.2024.10694427(113-116)Online publication date: 16-Sep-2024
https://doi.org/10.1109/ELMAR62909.2024.10694427
Häring SFolawiyo SPodguzova Mkrauß SStricker D(2024)Vid2Cuts: A Framework for Enabling AI-Guided Grapevine PruningIEEE Access10.1109/ACCESS.2024.335043212(5814-5836)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3350432
Antić DTiwari GOzcomlekci BMarin RPons-Moll G(2024)CloSe: A 3D Clothing Segmentation Dataset and Model2024 International Conference on 3D Vision (3DV)10.1109/3DV62453.2024.00020(591-601)Online publication date: 18-Mar-2024
https://doi.org/10.1109/3DV62453.2024.00020
Tverdomed VGorban AKushmar L(2024)Image segmentation method of rail head defects and area measurement of selected segmentsMATEC Web of Conferences10.1051/matecconf/202439004008390(04008)Online publication date: 24-Jan-2024
https://doi.org/10.1051/matecconf/202439004008
Zhang YChen JMa XWang GBhatti UHuang M(2024)Interactive medical image annotation using improved Attention U-net with compound geodesic distanceExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121282237:PAOnline publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121282
Li YZheng DLiu Y(2024)Box-supervised dynamical instance segmentation for in-field cottonComputers and Electronics in Agriculture10.1016/j.compag.2023.108390215:COnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.compag.2023.108390
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Figures

Tables

Media

View Table of Conten