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) 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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  1. Chambolle, Antonin; Pock, Thomas: Crouzeix-Raviart approximation of the total variation on simplicial meshes (2020)
  2. Falcão, Alexandre; Bragantini, Jordão: The role of optimum connectivity in image segmentation: can the algorithm learn object information during the process? (2019)
  3. Roberts, Michael; Spencer, Jack: Chan-Vese reformulation for selective image segmentation (2019)
  4. Singh, Surya Kant; Srivastava, Rajeev: A novel probabilistic contrast-based complex salient object detection (2019)
  5. Zhu, Jie; Zhang, Yunfeng; Guo, Jie; Liu, Huikun; Liu, Mingming; Liu, Yang; Guo, Yanwen: Label transfer between images and 3D shapes via local correspondence encoding (2019)
  6. Chen, Da; Cohen, Laurent D.: Fast asymmetric fronts propagation for image segmentation (2018)
  7. Cousty, Jean; Najman, Laurent; Kenmochi, Yukiko; Guimarães, Silvio: Hierarchical segmentations with graphs: quasi-flat zones, minimum spanning trees, and saliency maps (2018)
  8. Huan, Guoqiang; Song, Zhanjie; Zhang, Shuo; Zhu, Jianhua: A fast marine sewage detection method for remote-sensing image (2018)
  9. Larsson, Måns; Arnab, Anurag; Zheng, Shuai; Torr, Philip; Kahl, Fredrik: Revisiting deep structured models for pixel-level labeling with gradient-based inference (2018)
  10. Harizanov, Stanislav; Margenov, Svetozar; Marinov, Pencho; Vutov, Yavor: Volume constrained 2-phase segmentation method utilizing a linear system solver based on the best uniform polynomial approximation of (x^- 1/2) (2017)
  11. Kchaou, Mourad; Al Ahmadi, Saleh: Robust (H_\infty) control for nonlinear uncertain switched descriptor systems with time delay and nonlinear input: a sliding mode approach (2017)
  12. Fercoq, Olivier; Richtárik, Peter: Optimization in high dimensions via accelerated, parallel, and proximal coordinate descent (2016)
  13. Khan, Salman H.; Bennamoun, Mohammed; Sohel, Ferdous; Togneri, Roberto; Naseem, Imran: Integrating geometrical context for semantic labeling of indoor scenes using RGBD images (2016)
  14. Toutain, Matthieu; Elmoataz, Abderrahim; Lozes, François; Mansouri, Alamin: Non-local discrete (\infty)-Poisson and Hamilton Jacobi equations. From stochastic game to generalized distances on images, meshes, and point clouds (2016)
  15. Wu, Weiwei; Zhou, Zhuhuang; Wu, Shuicai; Zhang, Yanhua: Automatic liver segmentation on volumetric CT images using supervoxel-based graph cuts (2016)
  16. Afifi, Mahmoud; Hussain, Khaled F.: MPB: a modified Poisson blending technique (2015)
  17. Averbuch-Elor, Hadar; Cohen-Or, Daniel: Ringit, ring-ordering casual photos of a temporal event (2015)
  18. Borji, Ali: What is a salient object? A dataset and a baseline model for salient object detection (2015)
  19. Diebold, Julia; Demmel, Nikolaus; Hazırbaş, Caner; Moeller, Michael; Cremers, Daniel: Interactive multi-label segmentation of RGB-D images (2015)
  20. Li, Hong; Wu, Wen; Wu, Enhua: Robust interactive image segmentation via graph-based manifold ranking (2015)

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