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Cosegmentation Revisited: Models and Optimization

  • Sara Vicente
  • Vladimir Kolmogorov
  • Carsten Rother
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6312)

Abstract

The problem of cosegmentation consists of segmenting the same object (or objects of the same class) in two or more distinct images. Recently a number of different models have been proposed for this problem. However, no comparison of such models and corresponding optimization techniques has been done so far. We analyze three existing models: the L1 norm model of Rother et al. [1], the L2 norm model of Mukherjee et al. [2] and the “reward” model of Hochbaum and Singh [3]. We also study a new model, which is a straightforward extension of the Boykov-Jolly model for single image segmentation [4].

In terms of optimization, we use a Dual Decomposition (DD) technique in addition to optimization methods in [1,2]. Experiments show a significant improvement of DD over published methods. Our main conclusion, however, is that the new model is the best overall because it: (i) has fewest parameters; (ii) is most robust in practice, and (iii) can be optimized well with an efficient EM-style procedure.

Keywords

Color Histogram Appearance Model Foreground Object Subgradient Method Global Term 
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.

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References

  1. 1.
    Rother, C., Kolmogorov, V., Minka, T., Blake, A.: Cosegmentation of image pairs by histogram matching - incorporating a global constraint into MRFs. In: CVPR (2006)Google Scholar
  2. 2.
    Mukherjee, L., Singh, V., Dyer, C.R.: Half-integrality based algorithms for cosegmentation of images. In: CVPR (2009)Google Scholar
  3. 3.
    Hochbaum, D.S., Singh, V.: An efficient algorithm for co-segmentation. In: ICCV (2009)Google Scholar
  4. 4.
    Boykov, Y., Jolly, M.P.: Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. In: ICCV (2001)Google Scholar
  5. 5.
    Cui, J., Yang, Q., Wen, F., Wu, Q., Zhang, C., Cool, L.V., Tang, X.: Transductive object cutout. In: CVPR (2008)Google Scholar
  6. 6.
    Batra, D., Kowdle, A., Parikh, D., Luo, J., Chen, T.: iCoseg: Interactive co-segmentation with intelligent scribble guidance. In: CVPR (2010)Google Scholar
  7. 7.
    Winn, J., Jojic, N.: Locus: learning object classes with unsupervised segmentation. In: ICCV (2005)Google Scholar
  8. 8.
    Joulin, A., Bach, F., Ponce, J.: Discriminative clustering for image co-segmentation. In: CVPR (2010)Google Scholar
  9. 9.
    Batra, D., Parikh, D., Kowdle, A., Chen, T., Luo, J.: Seed image selection in interactive cosegmentation. In: ICIP (2009)Google Scholar
  10. 10.
    Personal communication with Vikas SinghGoogle Scholar
  11. 11.
    Rother, C., Kolmogorov, V., Blake, A.: Grabcut - interactive foreground extraction using iterated graph cuts. In: SIGGRAPH (2004)Google Scholar
  12. 12.
    Vicente, S., Kolmogorov, V., Rother, C.: Joint optimization of segmentation and appearance models. In: ICCV (2009)Google Scholar
  13. 13.
    Hammer, P.L., Hansen, P., Simeone, B.: Roof duality, complementation and persistency in quadratic 0-1 optimization. Math. Programming 28, 121–155 (1984)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Boros, E., Hammer, P.L.: Pseudo-boolean optimization. Discrete Applied Mathematics 123(1-3), 155–225 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Bertsekas, D.: Nonlinear Programming. Athena Scientific, Belmont(1999)Google Scholar
  16. 16.
    Wainwright, M., Jaakkola, T., Willsky, A.: MAP estimation via agreement on trees: Message-passing and linear-programming approaches. IEEE Trans. Information Theory 51(11), 3697–3717 (2005)CrossRefMathSciNetGoogle Scholar
  17. 17.
    Schlesinger, M.I., Giginyak, V.V.: Solution to structural recognition (MAX,+)-problems by their equivalent transformations. Part 1. In: Control Systems and Computers, pp. 3–15 (2007)Google Scholar
  18. 18.
    Schlesinger, M.I., Giginyak, V.V.: Solution to structural recognition (MAX,+)-problems by their equivalent transformations. Part 2. In: Control Systems and Computers, pp. 3–18 (2007)Google Scholar
  19. 19.
    Komodakis, N., Paragios, N., Tziritas, G.: MRF optimization via dual decomposition: Message-passing revisited. In: ICCV (2005)Google Scholar
  20. 20.
    Rhemann, C., Rother, C., Wang, J., Gelautz, M., Kohli, P., Rott, P.: A perceptually motivated online benchmark for image matting. In: CVPR (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Sara Vicente
    • 1
  • Vladimir Kolmogorov
    • 1
  • Carsten Rother
    • 2
  1. 1.University College London 
  2. 2.Microsoft Research Cambridge 

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