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Asian Conference on Computer Vision

ACCV 2016: Computer Vision – ACCV 2016 pp 67–81Cite as

Generalized Fusion Moves for Continuous Label Optimization

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10115))

Abstract

Energy-minimization methods are ubiquitous in computer vision and related fields. Low-level computer vision problems are typically defined on regular pixel lattices and seek to assign discrete or continuous values (or both) to each pixel such that a combined data term and a spatial smoothness prior are minimized. In this work we propose to minimize difficult energies using repeated generalized fusion moves. In contrast to standard fusion moves, the fusion step optimizes over binary and continuous sets of variables representing label ranges. Further, each fusion step can optimize over additional continuous unknowns. We demonstrate the general method on a variational-inspired stereo approach, and optionally optimize over radiometric changes between the images as well.

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References

  1. Zach, C., Kohli, P.: A convex discrete-continuous approach for Markov random fields. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7577, pp. 386–399. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33783-3_28

    Chapter  Google Scholar 

  2. Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23, 1222–1239 (2001)

    Article  Google Scholar 

  3. Gould, S., Amat, F., Koller, D.: Alphabet SOUP: a framework for approximate energy minimization. In: Proceedings of CVPR, pp. 903–910 (2009)

    Google Scholar 

  4. Carr, P., Hartley, R.: Solving multilabel graph cut problems with multilabel swap. In: 2009 Digital Image Computing: Techniques and Applications, DICTA 2009, pp. 532–539 (2009)

    Google Scholar 

  5. Schmidt, M., Alahari, K.: Generalized fast approximate energy minimization via graph cuts: alpha-expansion beta-shrink moves. In: Proceedings of UAI, pp. 653–660 (2011)

    Google Scholar 

  6. Veksler, O.: Graph cut based optimization for MRFs with truncated convex priors. In: Proceedings of CVPR (2007)

    Google Scholar 

  7. Kumar, M.P., Veksler, O., Torr, P.: Improved moves for truncated convex models. J. Mach. Learn. Res. 12, 31–67 (2011)

    MATH  Google Scholar 

  8. Veksler, O.: Multi-label moves for MRFs with truncated convex priors. Int. J. Comput. Vis. 98, 1–14 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  9. Jezierska, A., Talbot, H., Veksler, O., Wesierski, D.: A fast solver for truncated-convex priors: quantized-convex split moves. In: Boykov, Y., Kahl, F., Lempitsky, V., Schmidt, F.R. (eds.) EMMCVPR 2011. LNCS, vol. 6819, pp. 45–58. Springer, Heidelberg (2011). doi:10.1007/978-3-642-23094-3_4

    Google Scholar 

  10. Woodford, O., Reid, I., Torr, P., Fitzgibbon, A.: Fields of experts for image-based rendering. In: Proceedings of BMVC (2006)

    Google Scholar 

  11. Lempitsky, V., Rother, C., Blake, A.: Logcut–efficient graph cut optimization for Markov random fields. In: Proceedings of ICCV (2007)

    Google Scholar 

  12. Lempitsky, V., Rother, C., Roth, S., Blake, A.: Fusion moves for Markov random field optimization. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1392–1405 (2010)

    Article  Google Scholar 

  13. Woodford, O., Torr, P., Reid, I., Fitzgibbon, A.: Global stereo reconstruction under second-order smoothness priors. IEEE Trans. Pattern Anal. Mach. Intell. 31, 2115–2128 (2009)

    Article  Google Scholar 

  14. Ishikawa, H.: Higher-order gradient descent by fusion-move graph cut. In: Proceedings of ICCV (2009)

    Google Scholar 

  15. Trobin, W., Pock, T., Cremers, D., Bischof, H.: Continuous energy minimization via repeated binary fusion. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5305, pp. 677–690. Springer, Heidelberg (2008). doi:10.1007/978-3-540-88693-8_50

    Chapter  Google Scholar 

  16. Olsson, C., Byrod, M., Overgaard, N., Kahl, F.: Extending continuous cuts: anisotropic metrics and expansion moves. In: Proceedings of CVPR, pp. 405–412 (2009)

    Google Scholar 

  17. Zach, C.: Dual decomposition for joint discrete-continuous optimization. In: Proceedings of AISTATS (2013)

    Google Scholar 

  18. Fix, A., Agarwal, S.: Duality and the continuous graphical model. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 266–281. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10578-9_18

    Google Scholar 

  19. Möllenhoff, T., Laude, E., Moeller, M., Lellmann, J., Cremers, D.: Sublabel-accurate relaxation of nonconvex energies. In: Proceedings of CVPR (2016)

    Google Scholar 

  20. Dacorogna, B., Maréchal, P.: The role of perspective functions in convexity, polyconvexity, rank-one convexity and separate convexity. J. Convex Anal. 15, 271–284 (2008)

    MathSciNet  MATH  Google Scholar 

  21. Kovtun, I.: Partial optimal labeling search for a NP-hard subclass of (max, +) problems. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 402–409. Springer, Heidelberg (2003). doi:10.1007/978-3-540-45243-0_52

    Chapter  Google Scholar 

  22. Kovtun, I.: Sufficient condition for partial optimality for (max, +) labeling problems and its usage. Technical report, International Research and Training Centre for Information Technologies and Systems (2010)

    Google Scholar 

  23. Shekhovtsov, A., Hlavac, V.: On partial optimality by auxiliary submodular problems. In: Control Systems and Computers, no. 2 (2011)

    Google Scholar 

  24. Desmet, J., Maeyer, M.D., Hazes, B., Lasters, I.: The dead-end elimination theorem and its use in protein side-chain positioning. Nature 356, 539–542 (1992)

    Article  Google Scholar 

  25. Georgiev, I., Lilien, R.H., Donald, B.R.: The minimized dead-end elimination criterion and its application to protein redesign in a hybrid scoring and search algorithm for computing partition functions over molecular ensembles. J. Comput. Chem. 29, 1527–1542 (2008)

    Article  MATH  Google Scholar 

  26. Gainza, P., Roberts, K.E., Donald, B.R.: Protein design using continuous rotamers. PLoS Comput. Biol. 8, e1002335 (2012)

    Article  Google Scholar 

  27. Zach, C.: A principled approach for coarse-to-fine map inference. In: Proceedings of CVPR, pp. 1330–1337 (2014)

    Google Scholar 

  28. Pock, T., Chambolle, A.: Diagonal preconditioning for first order primal-dual algorithms in convex optimization. In: Proceedings of ICCV, pp. 1762–1769 (2011)

    Google Scholar 

  29. Seitz, S., Baker, S.: Filter flow. In: Proceedings of ICCV, pp. 143–150 (2009)

    Google Scholar 

  30. Hirschmüller, H., Scharstein, D.: Evaluation of stereo matching costs on images with radiometric differences. IEEE Trans. Pattern Anal. Mach. Intell. 31, 1582–1599 (2009)

    Article  Google Scholar 

  31. Strecha, C., Tuytelaars, T., Van Gool, L.: Dense matching of multiple wide-baseline views. In: Proceedings of ICCV, pp. 1194–1201 (2003)

    Google Scholar 

  32. Sizintsev, M., Wildes, R.: Efficient stereo with accurate 3-D boundaries. Proc. BMVC 25(1–25), 10 (2006)

    Google Scholar 

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Authors and Affiliations

  1. Toshiba Research Europe, Cambridge, UK

    Christopher Zach

Authors
  1. Christopher Zach
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Correspondence to Christopher Zach .

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Editors and Affiliations

  1. National Tsing Hua University, Hsinchu, Taiwan

    Shang-Hong Lai

  2. Graz University of Technology, Graz, Austria

    Vincent Lepetit

  3. Drexel University, Philadelphia, Pennsylvania, USA

    Ko Nishino

  4. The University of Tokyo, Tokyo, Japan

    Yoichi Sato

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Zach, C. (2017). Generalized Fusion Moves for Continuous Label Optimization. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10115. Springer, Cham. https://doi.org/10.1007/978-3-319-54193-8_5

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  • DOI: https://doi.org/10.1007/978-3-319-54193-8_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54192-1

  • Online ISBN: 978-3-319-54193-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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