Point-Cut: Interactive Image Segmentation Using Point Supervision

  • Changjae Oh
  • Bumsub Ham
  • Kwanghoon SohnEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10111)


Interactive image segmentation is a fundamental task in many applications in graphics, image processing, and computational photography. Many leading methods formulate elaborated energy functionals, achieving high performance with reflecting human’s intention. However, they show limitations in practical usage since user interaction is labor intensive to obtain segments efficiently. We present an interactive segmentation method to handle this problem. Our approach, called point cut, requires minimal point supervision only. To this end, we use off-the-shelf object proposal methods that generate object candidates with high recall. With the single point supervision, foreground appearance can be estimated with high accuracy, and then integrated into a graph cut optimization to generate binary segments. Intensive experiments show that our approach outperforms existing methods for interactive object segmentation both qualitatively and quantitatively.


Gaussian Mixture Model Appearance Model Salient Object Foreground Object Object Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by Institute for Information and communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. R0115-15-1007, High quality 2d-to-multiview contents generation from large-scale RGB+D database).


  1. 1.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE TPAMI 22(8), 805–888 (2000)Google Scholar
  2. 2.
    Carreira, J., Sminchisescu, C.: CPMC: automatic object segmentation using constrained parametric min-cuts. IEEE TPAMI 34(7), 1312–1328 (2012)CrossRefGoogle Scholar
  3. 3.
    Uijlings, J.R., van de Sande, K.E., Gevers, T., Smeulders, A.W.: Selective search for object recognition. IJCV 104(2), 154–171 (2013)CrossRefGoogle Scholar
  4. 4.
    Manén, S., Guillaumin, M., Gool, L.: Prime object proposals with randomized prim’s algorithm. In: ICCV (2013)Google Scholar
  5. 5.
    Arbeláez, P., Pont-Tuset, J., Barron, J., Marques, F., Malik, J.: Multiscale combinatorial grouping. In: CVPR (2014)Google Scholar
  6. 6.
    Krähenbühl, P., Koltun, V.: Geodesic object proposals. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 725–739. Springer, Heidelberg (2014). doi: 10.1007/978-3-319-10602-1_47 Google Scholar
  7. 7.
    Ren, X., Malik, J.: Tracking as repeated figure/ground segmentation. In: CVPR (2007)Google Scholar
  8. 8.
    Cinbis, R., Verbeek, J., Schmid, C.: Segmentation driven object detection with Fisher vectors. In: ICCV (2013)Google Scholar
  9. 9.
    Rother, C., Kolmogorov, V., Blake, A.: Grabcut: Interactive foreground extraction using iterated graph cuts. In: ACM SIGGRAPH (2004)Google Scholar
  10. 10.
    Mortensen, E.N., Barret, W.A.: Intelligent scissors for image composition. In: ACM SIGGRAPH (1995)Google Scholar
  11. 11.
    Boykov, Y.Y., Jolly, M.P.: Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. In: ICCV (2001)Google Scholar
  12. 12.
    Grady, L.: Random walks for image segmentation. IEEE TPAMI 28(11), 1768–1783 (2006)CrossRefGoogle Scholar
  13. 13.
    Kim, T.H., Lee, K.M., Lee, S.U.: Generative image segmentation using random walks with restart. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5304, pp. 264–275. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-88690-7_20 CrossRefGoogle Scholar
  14. 14.
    Casaca, W., Nonato, L.G., Taubin, G.: Laplacian coordinates for seeded image segmentation. In: CVPR (2014)Google Scholar
  15. 15.
    Santner, J., Pock, T., Bischof, H.: Interactive multi-label segmentation. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010. LNCS, vol. 6492, pp. 397–410. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-19315-6_31 CrossRefGoogle Scholar
  16. 16.
    Borji, A., Cheng, M.M., Jiang, H., Li, J.: Salient object detection: a benchmark. IEEE TIP 24(12), 5706–5722 (2015)MathSciNetGoogle Scholar
  17. 17.
    Cheng, M.M., Mitra, N.J., Huang, X., Torr, P.H., Hu, S.: Global contrast based salient region detection. IEEE TPAMI 37(3), 569–582 (2015)CrossRefGoogle Scholar
  18. 18.
    Kim, T., Lee, K., Lee, S.: Nonparametric higher-order learning for interactive segmentation. In: CVPR (2010)Google Scholar
  19. 19.
    Wang, T., Han, B., Collomosse, J.: TouchCut: fast image and video segmentation using single-touch interaction. CVIU 120, 14–30 (2014)Google Scholar
  20. 20.
    Xu, J., Collins, M.D., Singh, V.: Incorporating user interaction and topological constraints within contour completion via discrete calculus. In: CVPR (2013)Google Scholar
  21. 21.
    Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)CrossRefGoogle Scholar
  22. 22.
    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: ICCV (2001)Google Scholar
  23. 23.
    Levin, A., Lischinski, D., Weiss, Y.: Colorization using Optimization. In: ACM SIGGRAPH (2004)Google Scholar
  24. 24.
    Chuang, Y.Y., Curless, B., Salesin, D.H., Szeliski, R.: A bayesian approach to digital matting. In: CVPR (2001)Google Scholar
  25. 25.
    An, X., Pellacini, F.: AppProp: all-pairs appearance-space edit propagation. In: ACM SIGGRAPH (2008)Google Scholar
  26. 26.
    Dai, J., He, K., Sun, J.: BoxSup: exploiting bounding boxes to supervise convolutional networks for semantic segmentation. In: ICCV (2015)Google Scholar
  27. 27.
    Lin, D., Dai, J., Jia, J., He, K., Sun, J.: ScribbleSup: scribble-supervised convolutional networks for semantic segmentation. In: CVPR (2016)Google Scholar
  28. 28.
    Lempitsky, V., Kohli, P., Rother, C., Sharp, T.: Image segmentation with a bounding box prior. In: ICCV (2009)Google Scholar
  29. 29.
    Tang, M., Gorelick, L., Veksler, O., Boykov, Y.: Grabcut in one cut. In: ICCV (2013)Google Scholar
  30. 30.
    Wu, J., Zhao, Y., Zhu, J., Luo, S., Tu, Z.: MILCut: A sweeping line multiple instance learning paradigm for interactive image segmentation. In: CVPR (2014)Google Scholar
  31. 31.
    Cheng, M.M., Prisacariu, V.A., Zheng, S., Torr, P.H., Rother, C.: DenseCut: densely connected CRFs for realtime GrabCut. In: Pacific Graphics (2015)Google Scholar
  32. 32.
    Yu, H., Zhou, Y., Qian, H., Xian, M., Lin, Y., Guo, D., Zheng, K., Abdelfatah, K., Wang, S.: LooseCut: interactive image segmentation with loosely bounded boxes. arXiv preprint arXiv:1507.03060 (2015)
  33. 33.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. IJCV 59(2), 167–181 (2004)CrossRefGoogle Scholar
  34. 34.
    Bai, J., Wu, X.: Error-tolerant scribbles based interactive image segmentation. In: CVPR (2014)Google Scholar
  35. 35.
    Liu, T., Yuan, Z., Sun, J., Wang, J., Zheng, N., Tang, X., Shum, H.Y.: Learning to detect a salient object. IEEE TPAMI 33(2), 353–367 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Yonsei UniversitySeoulRepublic of Korea

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