GrabCut: Interactive foreground extraction using iterated graph cuts
Open accessAbstract
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.
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GrabCut works with a user-provided bounding box and requires fewer interactions than Graph Cuts, but its performance is limited by the representativity of the underpinning GMM.
“GrabCut [9] works with a user-provided bounding box to start the segmentation and requires fewer interactions compared with Graph Cuts [8], but the performance is still limited by the representativity of the underpinning GMM.”
From DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation (2019) · cites this paper as [9]
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- Authors
- Rother, C., Kolmogorov, V., & Blake, A.
- Affiliations
- Microsoft Research (United Kingdom) (GB)
- Venue
- ACM Transactions on Graphics
- Published
- 2004-08
- Volume
- 23
- Issue
- 3
- Pages
- 309-314
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2124351162
- Cited by
- 5,703
- References
- 15
- Citations by year
- 2026: 83 · 2025: 180 · 2024: 205 · 2023: 256 · 2022: 248 · 2021: 346 · 2020: 326 · 2019: 333 · 2018: 351 · 2017: 379 · 2016: 407 · 2015: 416 · 2014: 391 · 2013: 401 · 2012: 302
- FWCI
- 88.03
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Transactions on Graphics (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Computer Science and Engineering
- Keywords
- foreground-background segmentation, GrabCut, graph cuts, interactive segmentation, alpha matte estimation, image editing, iterative optimization, user interaction
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