Abstract
Graph matching is a powerful tool for computer vision and machine learning. In this paper, a novel approach to graph matching is developed based on the sequential Monte Carlo framework. By constructing a sequence of intermediate target distributions, the proposed algorithm sequentially performs a sampling and importance resampling to maximize the graph matching objective. Through the sequential sampling procedure, the algorithm effectively collects potential matches under one-to-one matching constraints to avoid the adverse effect of outliers and deformation. Experimental evaluations on synthetic graphs and real images demonstrate its higher robustness to deformation and outliers.
Chapter PDF
Similar content being viewed by others
References
Duchenne, O., Joulin, A., Ponce, J.: A graph-matching kernel for object categorization. In: ICCV (2011)
Cao, Y., Zhang, Z., Czogiel, I., Dryden, I., Wang, S.: 2d nonrigid partial shape matching using mcmc and contour subdivision. In: CVPR (2011)
Birchfield, S.: KLT: An implementation of the kanade-lucas-tomasi feature tracker (1998)
Leordeanu, M., Hebert, M.: A spectral technique for correspondence problems using pairwise constraints. In: ICCV (2005)
Leordeanu, M., Hebert, M.: An integer projected fixed point method for graph matching and map inference. In: NIPS (2009)
Cho, M., Lee, J., Lee, K.M.: Reweighted Random Walks for Graph Matching. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 492–505. Springer, Heidelberg (2010)
Lee, J., Cho, M., Lee, K.M.: A graph matching algorithm using data-driven markov chain monte carlo sampling. In: ICPR (2010)
Duchenne, O., Bach, F., Kweon, I.S., Ponce, J.: A tensor-based algorithm for high-order graph matching. PAMI (2010)
Lee, J., Cho, M., Lee, K.M.: Hyper-graph matching via reweighted random walks. In: CVPR (2011)
Caetano, T., McAuley, J., Cheng, L., Le, Q., Smola, A.: Learning graph matching. PAMI (2009)
Leordeanu, M., Hebert, M.: Unsupervised learning for graph matching. In: CVPR (2009)
Cho, M., Lee, K.M.: Progressive graph matching: Making a move of graphs via probabilistic voting. In: CVPR (2012)
Cappé, O., Godsill, S., Moulines, E.: An overview of existing methods and recent advances in sequential monte carlo. Proc. IEEE (2007)
Maciel, J., Costeira, J.P.: A global solution to sparse correspondence problems. PAMI 25, 187–199 (2003)
Gold, S., Rangarajan, A.: A graduated assignment algorithm for graph matching. PAMI, 411–436 (1996)
Cour, T., Srinivasan, P., Shi, J.: Balanced graph matching. In: NIPS (2006)
Torresani, L., Kolmogorov, V., Rother, C.: Feature Correspondence Via Graph Matching: Models and Global Optimization. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 596–609. Springer, Heidelberg (2008)
Dellaert, F., Seitz, S.M., Thrun, S., Thorpe, C.: Feature correspondence: A markov chain monte carlo approach. In: NIPS (2001)
Tamminen, T., Lampinen, J.: Sequential monte carlo for bayesian matching of objects with occlusions. PAMI (2006)
Lu, C., Latecki, L.J., Adluru, N., Yang, X., Ling, H.: Shape guided contour grouping with particle filters. In: ICCV (2009)
Yang, X., Latecki, L.J.: Weakly Supervised Shape Based Object Detection with Particle Filter. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 757–770. Springer, Heidelberg (2010)
Isard, M., Blake, A.: Condensation - conditional density propagation for visual tracking. IJCV (1998)
Moral, P.D., Doucet, A.: Sequential monte carlo for bayesian computation. Journal of the Royal Statistical Society (2007)
Moral, P.D., Doucet, A., Jarsa, A.: Sequential monte carlo samplers. Journal of the Royal Statistical Society, 411–436 (2006)
Zass, R., Shashua, A.: Probabilistic graph and hypergraph matching. In: CVPR (2008)
Munkres, J.: Algorithms for the assignment and transportation problems. SIAM (1957)
Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: BMVC (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Suh, Y., Cho, M., Lee, K.M. (2012). Graph Matching via Sequential Monte Carlo. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds) Computer Vision – ECCV 2012. ECCV 2012. Lecture Notes in Computer Science, vol 7574. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33712-3_45
Download citation
DOI: https://doi.org/10.1007/978-3-642-33712-3_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33711-6
Online ISBN: 978-3-642-33712-3
eBook Packages: Computer ScienceComputer Science (R0)