Background Cut

  • Jian Sun
  • Weiwei Zhang
  • Xiaoou Tang
  • Heung-Yeung Shum
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3952)


In this paper, we introduce background cut, a high quality and real-time foreground layer extraction algorithm. From a single video sequence with a moving foreground object and stationary background, our algorithm combines background subtraction, color and contrast cues to extract a foreground layer accurately and efficiently. The key idea in background cut is background contrast attenuation, which adaptively attenuates the contrasts in the background while preserving the contrasts across foreground/background boundaries. Our algorithm builds upon a key observation that the contrast (or more precisely, color image gradient) in the background is dissimilar to the contrast across foreground/background boundaries in most cases. Using background cut, the layer extraction errors caused by background clutter can be substantially reduced. Moreover, we present an adaptive mixture model of global and per-pixel background colors to improve the robustness of our system under various background changes. Experimental results of high quality composite video demonstrate the effectiveness of our background cut algorithm.


Segmentation Result Background Image Color Model Foreground Object Segmentation Boundary 


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  1. 1.
    Elgammal, A., Harwood, D., Davis, L.: Non-parametric model for background subtraction. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 751–767. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  2. 2.
    Bergen, J.R., Burt, P.J., Hingorani, R., Peleg, S.: A three-frame algorithm for estimating two-component image motion. IEEE Trans. on PAMI 14, 886–896 (1992)CrossRefGoogle Scholar
  3. 3.
    Boykov, Y., Jolly, M.P.: Interactive graph cuts for optimal boundary & region segmentation of objects in n-d images. In: Proceedings of ICCV, pp. 105–112 (2001)Google Scholar
  4. 4.
    Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. In: Energy Minimization Methods in CVPR (2001)Google Scholar
  5. 5.
    Goldberger, J., Gordon, S., Greenspan, H.: An efficient image similarity measure based on approximations of kl-divergence between two gaussian mixtures. In: Proceedings of CVPR, pp. 487–494 (2004)Google Scholar
  6. 6.
    Grimson, W.E.L., Stauffer, C., Romano, R., Lee, L.: Using adaptive tracking to classify and monitor activities in a site. In: Proceedings of CVPR, pp. 22–29 (1998)Google Scholar
  7. 7.
    Koller, D., Weber, J., Malik, J.: Robust multiple car tracking with occlusion reasoning. In: Proceedings of ECCV, pp. 189–196 (1993)Google Scholar
  8. 8.
    Kolmogorov, V., Criminisi, A., Blake, A., Cross, G., Rother, C.: Bi-layer segmentation of binocular stereo video. In: Proceedings of CVPR, pp. 1186–1193 (2005)Google Scholar
  9. 9.
    Li, Y., Sun, J., Shum, H.Y.: Video object cut and paste. In: Proceedings of ACM SIGGRAPH, pp. 595–600 (2005)Google Scholar
  10. 10.
    Li, Y., Sun, J., Tang, C.K., Shum, H.Y.: Lazy snapping. In: Proceedings of ACM SIGGRAPH (2004)Google Scholar
  11. 11.
    Mittal, A., Paragios, N.: Motion-based background subtraction using adaptive kernel density estimation. In: Proceedings of CVPR, pp. 302–309 (2004)Google Scholar
  12. 12.
    Monnet, A., Mittal, A., Paragios, N., Ramesh, V.: Background modeling and subtraction of dynamic scenes. In: Proceedings of ICCV, pp. 1305–1312 (2005)Google Scholar
  13. 13.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Tran. on PAMI 12, 629–663 (1990)CrossRefGoogle Scholar
  14. 14.
    Ren, Y., Chua, C.S., Ho, Y.-K.: Motion detection with non-stationary background. In: Machine Vision and Applications, pp. 332–343 (2003)Google Scholar
  15. 15.
    Rother, C., Blake, A., Kolmogorov, V.: Grabcut - interactive foreground extraction using iterated graph cuts. In: Proceedings of ACM SIGGRAPH, pp. 309–314 (2004)Google Scholar
  16. 16.
    Sheikh, Y., Shah, M.: Bayesian object detection in dynamic scenes. In: Proceedings of CVPR, pp. 1778–1792 (2005)Google Scholar
  17. 17.
    Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: principles and practice of background maintenance. In: Proceedings of ICCV, pp. 255–261 (1999)Google Scholar
  18. 18.
    Tuzel, O., Porikli, F., Meer, P.: A bayesian approach to background modeling. In: IEEE Workshop on Machine Vision for Intelligent Vehicles (2005)Google Scholar
  19. 19.
    Wang, J., Bhat, P., Colburn, R.A., Agrawala, M., Cohen, M.F.: Interactive video cutout. In: Proceedings of ACM SIGGRAPH, pp. 585–594 (2005)Google Scholar
  20. 20.
    Wang, J.Y.A., Adelson, E.H.: Layered representation for motion analysis. In: Proceedings of CVPR, pp. 361–366 (1993)Google Scholar
  21. 21.
    Wills, J., Agarwal, S., Belongie, S.: What went where. In: Proceedings of CVPR, pp. 37–44 (2003)Google Scholar
  22. 22.
    Wren, C., Azarbayejani, A., Darrell, T., Pentland, A.: Pfinder: Real-time tracking of the human body. IEEE Tran. on PAMI 19, 780–785 (1997)CrossRefGoogle Scholar
  23. 23.
    Xiao, J.J., Shah, M.: Motion layer extraction and alpha matting. In: Proceedings of CVPR, pp. 698–703 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jian Sun
    • 1
  • Weiwei Zhang
    • 1
  • Xiaoou Tang
    • 1
  • Heung-Yeung Shum
    • 1
  1. 1.Microsoft Research AsiaBeijingP.R. China

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