Abstract
This paper proposes a framework that provides significant speed-ups and also improves the effectiveness of general message passing algorithms based on dual LP relaxations. It is applicable to both pairwise and higher order MRFs, as well as to any type of dual relaxation. It relies on combining two ideas. The first one is inspired by algebraic multigrid approaches for linear systems, while the second one employs a novel decimation strategy that carefully fixes the labels for a growing subset of nodes during the course of a dual LP-based algorithm. Experimental results on a wide variety of vision problems demonstrate the great effectiveness of this framework.
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References
Wainwright, M., Jaakkola, T., Willsky, A.: Map estimation via agreement on trees: message-passing and linear programming. IEEE Trans. on Info. Theory (2005)
Kolmogorov, V.: Convergent tree-reweighted message passing for energy minimization. PAMI (2006)
Komodakis, N., Paragios, N., Tziritas, G.: MRF optimization via dual decomposition: Message-passing revisited. In: ICCV (2007)
Werner, T.: A linear programming approach to max-sum problem: A review. PAMI (2007)
Szeliski, R., et al.: A comparative study of energy minimization methods for markov random fields with smoothness-based priors. PAMI (2008)
Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient belief propagation for early vision. IJCV 70, 41–54 (2006)
Komodakis, N., Tziritas, G.: Image completion using efficient belief propagation via priority scheduling and dynamic pruning. In: IEEE TIP (2007)
Braunstein, A., Mézard, M., Zecchina, R.: Survey propagation: An algorithm for satisfiability. Random Struct. Algorithms 27, 201–226 (2005)
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)
Alahari, K., Kohli, P., Torr, P.: Reduce, reuse and recycle: Efficiently solving multi-label mrfs. In: CVPR (2008)
Shekhovtsov, A., Kovtun, I., Hlaváč, V.: Efficient mrf deformation model for non-rigid image matching. In: CVIU (2008)
http://www.csd.uoc.gr/~komod/publications/docs/eccv10_supp.pdf
Dykstra, R.L.: An iterative procedure for obtaining i-projections onto the intersection of convex sets. Annals of Probability (1985)
Komodakis, N., Paragios, N.: Beyond pairwise energies: Efficient optimization for higher-order MRFs. In: CVPR (2009)
Meltzer, T., Yanover, C., Weiss, Y.: Globally optimal solutions for energy minimization in stereo vision using reweighted belief propagation. In: ICCV (2005)
Kohli, P., Kumar, P., Torr, P.: P3 and beyond: Solving energies with higher order cliques. In: CVPR (2007)
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Komodakis, N. (2010). Towards More Efficient and Effective LP-Based Algorithms for MRF Optimization. In: Daniilidis, K., Maragos, P., Paragios, N. (eds) Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science, vol 6312. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15552-9_38
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DOI: https://doi.org/10.1007/978-3-642-15552-9_38
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