research-article

A Short Survey of Recent Advances in Graph Matching

Authors:

Xiaokang YangAuthors Info & Claims

ICMR '16: Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval

Pages 167 - 174

https://doi.org/10.1145/2911996.2912035

Published: 06 June 2016 Publication History

Abstract

Graph matching, which refers to a class of computational problems of finding an optimal correspondence between the vertices of graphs to minimize (maximize) their node and edge disagreements (affinities), is a fundamental problem in computer science and relates to many areas such as combinatorics, pattern recognition, multimedia and computer vision. Compared with the exact graph (sub)isomorphism often considered in a theoretical setting, inexact weighted graph matching receives more attentions due to its flexibility and practical utility. A short review of the recent research activity concerning (inexact) weighted graph matching is presented, detailing the methodologies, formulations, and algorithms. It highlights the methods under several key bullets, e.g. how many graphs are involved, how the affinity is modeled, how the problem order is explored, and how the matching procedure is conducted etc. Moreover, the research activity at the forefront of graph matching applications especially in computer vision, multimedia and machine learning is reported. The aim is to provide a systematic and compact framework regarding the recent development and the current state-of-the-arts in graph matching.

References

[1]

K. Adamczewski, Y. Suh, and K. Lee. Discrete tabu search for graph matching. In ICCV, 2015.

Digital Library

[2]

Y. Aflalo, A. Bronstein, and R. Kimmel. On convex relaxation of graph isomorphism. Proceedings of the National Academy of Sciences, 112(10):2942--2947, 2015.

[3]

A. Albarelli, S. Bulo, A. Torsello, and M. Pelillo. Matching as a non-cooperative game. In ICCV, pages 1319--1326, 2009.

[4]

H. A. Almohamad and S. O. Duffuaa. A linear programming approach for the weighted graph matching problem. PAMI, 1993.

Digital Library

[5]

A. Anwar and A. N. Mahmood. Anomaly detection in electric network database of smart grid: Graph matching approach. Electric Power Systems Research, 133:51--62, 2016.

[6]

A.S.Deshmukh, A. G. Banerjee, S. GuPta, and R. Sriram. Content-based assembly search: A step towards assembly reuse. ComPuter-Aided Design, pages 244--261, 2008.

Digital Library

[7]

S. Auwatanamongkol. Inexact graph matching using a genetic algorithm for image recognition. Pattern Recognition Letters, 28(12):1428--1437, 2007.

Digital Library

[8]

M. Bansal and K. Daniilidis. Geometric polynomial constraints in higher-order graph matching. arXiv preprint arXiv:1405.6261, 2014.

[9]

S. Berretti, A. D. Bimbo, and E. Vicario. Efficient matching and indexing of graph models in content-based retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(10):1089--1105, 2001.

Digital Library

[10]

S. Bougleux, L. Brun, V. Carletti, P. Foggia, B. Gaüzère, and M. Vento. A quadratic assignment formulation of the graph edit distance. arXiv preprint arXiv:1512.07494, 2015.

[11]

M. Brocheler, A. Pugliese, and V. Subrahmanian. Dogma: A disk-oriented graph matching algorithm for rdf database. In ISWC, 2009.

Digital Library

[12]

S. R. Bulò, M. Pelillo, and I. M. Bomze. Graph-based quadratic optimization: A fast evolutionary approach. Computer Vision and Image Understanding, 115(7):984--995, 2011.

Digital Library

[13]

H. Bunke. Error-tolerant graph matching: a formal framework and algorithms. In Advances in Pattern Recognition, pages 1--14. 1998.

Digital Library

[14]

H. Bunke. Error correcting graph matching: on the influence of the underlying cost function. PAMI, 21(9), 1999.

Digital Library

[15]

H. Bunke. Graph matching: theoretical foundations, algorithms, and applications. In Vision Interface, 2000.

[16]

R. Burkard, S. Karisch, and F. Rendl. Qaplib--a quadratic assignment problem library. Journal of Global optimization, 10(4):391--403, 1997.

Digital Library

[17]

T. Caetano, J. McAuley, L. Cheng, Q. Le, and A. J. Smola. Learning graph matching. TPAMI, 31(6):1048--1058, 2009.

Digital Library

[18]

R. M. Cesar, E. Bengoetxea, I. Bloch, and P. Larranaga. Inexact graph matching for model-based recognition: Evaluation and comparison of optimization algorithms. Pattern Recognition, 38(11):2099--2113, 2005.

Digital Library

[19]

M. C. Chang and B. B. Kimia. Measuring 3d shape similarity by graph-based matching of the medial scaffolds. Computer Vision and Image Understanding, pages 707--720, 2011.

Digital Library

[20]

Y. Chen, G. Leonidas, and Q. Huang. Matching partially similar objects via matrix completion. In ICML, 2014.

[21]

M. Chertok and Y. Keller. Efficient high order matching. TPAMI, 2010.

Digital Library

[22]

F. Chevalier, J. Domenger, J. Benois-Pineau, and M. Delest. Retrieval of objects in video by similarity based on graph matching. Pattern Recognition Letters, 28(8):939--949, 2007.

Digital Library

[23]

M. Cho, K. Alahari, and J. Ponce. Learning graphs to match. In ICCV, 2013.

Digital Library

[24]

M. Cho, S. Kwak, C. Schmid, and J. Ponce. Unsupervised object discovery and localization in the wild: Part-based matching with bottom-up region proposals. In CVPR.

[25]

M. Cho, J. Lee, and K. M. Lee. Reweighted random walks for graph matching. In ECCV, 2010.

Digital Library

[26]

M. Cho and K. M. Lee. Progressive graph matching: Making a move of graphs via probabilistic voting. In CVPR, 2012.

Digital Library

[27]

M. Cho, J. Sun, O. Duchenne, and J. Ponce. Finding matches in a haystack: a max-pooling strategy for graph matching in the presence of outliers. In CVPR, 2014.

Digital Library

[28]

H. Chui and A. Rangarajan. A new point matching algorithm for non-rigid registration. CVIU, 2003.

Digital Library

[29]

T. Collins, P. Mesejo, and A. Bartoli. An analysis of errors in graph-based keypoint matching and proposed solutions. In ECCV, 2014.

[30]

D. Conte, P. Foggia, C. Sansone, and M. Vento. Thirty years of graph matching in pattern recognition. IJPRAI, 2004.

[31]

D. Conte, P. Foggia, C. Sansone, and M. Vento. How and why pattern recognition and computer vision applications use graphs. In Applied Graph Theory in Computer Vision and Pattern Recognition, pages 85--135. Springer, 2007.

[32]

D. J. Cook and L. B. Holder. Mining graph data. John Wiley & Sons, 2006.

[33]

L. Cordella, P. Foggia, C. Sansone, and M. Vento. Fast graph matching for detecting cad image components. In ICPR, 2000.

[34]

X. Cortés and F. Serratosa. Learning graph-matching edit-costs based on the optimality of the oracle's node correspondences. Pattern Recognition Letters, 56:22--29, 2015.

Digital Library

[35]

T. Cour, P. Srinivasan, and J. Shi. Balanced graph matching. In NIPS, 2006.

Digital Library

[36]

S. Deb, M. Yeddanapudi, K. Pattipati, and Y. Bar-Shalom. A generalized sd assignment algorithm for multisensor-multitarget state estimation. IEEE Transactions on Aerospace and Electronic Systems, 33(2):523--538, 1997.

[37]

M. F. Demirci, B. Platel, A. Shokoufandeh, L. L. Florack, and S. J. Dickinson. The representation and matching of images using top points. Journal of Mathematical Imaging and Vision, 35(2):103--116, 2009.

Digital Library

[38]

R. Dijkman, M. Dumas, Z. Gao, and L. Garcia-Banuelos. Graph matching algorithms for business process model similarity search. In Business Process Management, pages 48--63. Springer, 2009.

[39]

O. Duchenne, F. Bach, I. Kweon, and J. Ponce. A tensor-based algor ithm for high-order graph matching. PAMI, 2011.

Digital Library

[40]

O. Duchenne, A. Joulin, and J. Ponce. A graph-matching kernel for object categorization. In ICCV, 2011.

Digital Library

[41]

A. Egozi, Y. Keller, and H. Guterman. A probabilistic approach to spectral graph matching. TPAMI, 2013.

Digital Library

[42]

D. Eppstein. Subgraph isomorphism in planar graphs and related problems. In SODA, 1995.

Digital Library

[43]

M. Eshera and K.-S. Fu. An image understanding system using attributed symbolic representation and inexact graph-matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, (5):604--618, 1986.

Digital Library

[44]

M. Fiori, P. Sprechmann, J. Vogelstein, P. Muse, and G. Sapiro. Robust multimodal graph matching: Sparse coding meets graph matching. In NIPS, 2013.

[45]

M. A. Fischler and R. C. Bolles. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM, 1981.

Digital Library

[46]

P. Foggia, G. Percannella, and M. Vento. Graph matching and learning in pattern recognition in the last 10 years. IJPRAI, 33(1), 2014.

[47]

M. Frank and P. Wolfe. An algorithm for quadratic programming. Naval Research Logistics Quarterly, 1956.

[48]

X. Gao, B. Xiao, D. Tao, and X. Li. A survey of graph edit distance. Pattern Analysis and Applications, 2010.

[49]

M. R. Garey and D. S. Johnson. Computers and Intractability; A Guide to the Theory of NP-Completeness. Freeman and Co., NY, USA, 1990.

Digital Library

[50]

S. Gold and A. Rangarajan. A graduated assignment algorithm for graph matching. TPAMI, 1996.

Digital Library

[51]

G. Golub and C. Loan. Matrix computations. Johns Hopkins University Press, 1996.

[52]

M. Gori, M. Maggini, and L. Sarti. Exact and approximate graph matching using random walks. PAMI, 2005.

Digital Library

[53]

A. Hlaoui and S. Wang. A new algorithm for graph matching with application to content-based image retrieval. In Structural, Syntactic, and Statistical Pattern Recognition, pages 291--300. 2002.

Digital Library

[54]

N. Hu, R. M. Rustamov, and L. Guibas. Graph mmatching with anchor nodes: a learning approach. In CVPR, 2013.

Digital Library

[55]

Q. Huang, G. Zhang, L. Gao, S. Hu, A. Butscher, and L. Guibas. An optimization approach for extracting and encoding consistent maps in a shape collection. ACM Transactions on Graphics (TOG), 2012.

Digital Library

[56]

B. Huet and E. Hancock. Shape retrieval by inexact graph matching. In IEEE International Conference on Multimedia Computing and Systems, volume 1, pages 772--776, 1999.

Digital Library

[57]

U. Kang, M. Hebert, and S. Park. Fast and scalable approximate spectral graph matching for correspondence problems. Information Sciences, 220:306--318, 2013.

Digital Library

[58]

A. Kar, S. Tulsiani, J. Carreira, and J. Malik. Category-specific object reconstruction from a single image. In CVPR, pages 1966--1974, 2015.

[59]

A. Kitamoto, C. Zhou, and M. Takagi. Similarity retrieval of noaa satellite imagery by graph matching. In IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, pages 60--73, 1993.

[60]

E. L. Lawler. The quadratic assignment problem. Management Science, pages 586--599, 1963.

Digital Library

[61]

J. Lebrun, P. Gosselin, and S. Philipp-Foliguet. Inexact graph matching based on kernels for object retrieval in image databases. Image and Vision Computing, 29(11):716--729, 2011.

Digital Library

[62]

J. Lee, M. Cho, and K. Lee. A graph matching algorithm using data-driven markov chain monte carlo sampling. In ICPR, 2010.

Digital Library

[63]

J. Lee, M. Cho, and K. M. Lee. Hyper-graph matching via reweighted randomwalks. In CVPR, 2011.

Digital Library

[64]

M. Leordeanu and M. Hebert. A spectral technique for correspondence problems using pairwise constraints. In ICCV, 2005.

Digital Library

[65]

M. Leordeanu, M. Hebert, and R. Sukthankar. An integer projected fixed point method for graph matching and map inference. In NIPS, 2009.

Digital Library

[66]

M. Leordeanu, R. Sukthankar, and M. Hebert. Unsupervised learning for graph matching. Int. J. Comput. Vis., pages 28--45, 2012.

Digital Library

[67]

M. Leordeanu, A. Zanfir, and C. Sminchisescu. Semi-supervised learning and optimization for hypergraph matching. In ICCV, 2011.

Digital Library

[68]

G. Levi. A note on the derivation of maximal common subgraphs of two directed or undirected graphs. Calcolo, 9(4):341--352, 1973.

[69]

C. Li, Q. Liu, W. Dong, X. Zhu, J. Liu, and H. Lu. Human age estimation based on locality and ordinal information. IEEE Transaction on Cybernetics, 2015.

[70]

C. Li, Q. Liu, J. Liu, and H. Lu. Learning ordinal discriminative features for age estimation. In CVPR, 2012.

[71]

C. S. Liao, K. Lu, M. Baym, R. Singh, and B. Berger. Isorankn: spectral methods for global alignment of multiple protein networks. Bioinformatics, 2009.

Digital Library

[72]

L. Lin, Y. Lu, Y. Pan, and X. Chen. Integrating graph partitioning and matching for trajectory analysis in video surveillance. IEEE Transactions on Image Processing, 21(12):4844--4857, 2012.

Digital Library

[73]

Z. Liu and H. Qiao. Gncgcp - graduated nonconvexity and graduated concavity procedure. IEEE PAMI, 2014.

Digital Library

[74]

Z. Liu, H. Qiao, and L. Xu. An extended path following algorithm for graph-matching problem. TPAMI, pages 1451--1456, 2012.

Digital Library

[75]

Z. Liu, H. Qiao, X. Yang, and S. Hoi. Graph matching by simplified convex-concave relaxation procedure. IJCV, 2014.

Digital Library

[76]

L. Livi and A. Rizzi. The graph matching problem. Pattern Analysis and Applications, 16(3):253--283, 2013.

Digital Library

[77]

E. M. Loiola, N. M. de Abreu, P. O. Boaventura-Netto, P. Hahn, and T. Querido. A survey for the quadratic assignment problem. EJOR, pages 657--90, 2007.

[78]

E. M. Luks. Isomorphism of graphs of bounded valence can be tested in polynomial time. Journal of Computer and System Sciences, 1982.

[79]

V. Lyzinski, S. Adali, J. T. Vogelstein, Y. Park, and C. E. Priebe. Seeded graph matching via joint optimization of fidelity and commensurability. stat, 1050:16, 2014.

[80]

V. Lyzinski, D. E. Fishkind, M. Fiori, J. T. Vogelstein, C. E. Priebe, and G. Sapiro. Graph matching: Relax at your own risk. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(1):60--73, 2016.

Digital Library

[81]

S. Melnik, H. Garcia-Molina, and E. Rahm. A versatile graph matching algorithm and its application to schema matching. Data Engineering, 2002.

Digital Library

[82]

G. A. Mills-Tettey, A. Stentz, and M. B. Dias. The dynamic hungarian algorithm for the assignment problem with changing costs. 2007.

[83]

C. F. Moreno-Garcıa and F. Serratosa. Correspondence consensus of two sets of correspondences through optimisation functions. Pattern Analysis and Applications, pages 1--13, 2015.

[84]

P. Morrison and J. J. Zou. Inexact graph matching using a hierarchy of matching processes. Computational Visual Media, 1(4):291--307, 2015.

[85]

J. Munkres. Algorithms for the assignment and transportation problems. Journal of the Society for Industrial & Applied Mathematics, 5(1):32--38, 1957.

[86]

M. Neuhaus and H. Bunke. Self-organizing maps for learning the edit costs in graph matching. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 35(3):503--514, 2005.

Digital Library

[87]

M. Neuhaus and H. Bunke. Automatic learning of cost functions for graph edit distance. Information Sciences, 177(1):239--247, 2007.

[88]

M. Neuhaus and H. Bunke. A quadratic programming approach to the graph edit distance problem. In Graph-Based Representations in Pattern Recognition, pages 92--102. 2007.

Digital Library

[89]

Q. Ngoc, A. Gautier, and M. Hein. A flexible tensor block coo rd inate ascent scheme for hypergraph matching. In CVPR, 2015.

[90]

W. Nie, A. Liu, Z. Gao, and Y. Su. Clique-graph matching by preserving global & local structure. In CVPR, 2015.

[91]

W. Nie, A. Liu, Y. Su, H. Luan, Z. Yang, L. Cao, and R. Ji. Single/cross-camera multiple-person tracking by graph matching. Neurocomputing, 2014.

Digital Library

[92]

Z. Niu, R. Martin, F. Langbein, and M. Sabin. Rapidly finding cad features using database optimization. Computer-Aided Design, 69:35--50, 2015.

Digital Library

[93]

D. Pachauri, R. Kondor, and S. Vikas. Solving the multi-way matching problem by permutation synchronization. In NIPS, 2013.

[94]

S. Park, S. Park, and M. Heber. Fast and scalable approximate spectral matching for higher-order graph matching. PAMI, 2013.

Digital Library

[95]

A. Poore. Multidimensional assignment formulation of data association problems arising from multitarget and multisensor tracking. Computational Optimization and Applications, 3(1):27--57, 1994.

Digital Library

[96]

C. E. Priebe, D. J. Marchette, Z. Ma, S. Adali, et al. Manifold matching: Joint optimization of fidelity and commensurability. Brazilian Journal of Probability and Statistics, 27(3):377--400, 2013.

[97]

A. Rangarajan, A. Yuille, and E. Mjolsness. Convergence properties of the softassign quadratic assignment algorithm. Neural Computation, 1999.

Digital Library

[98]

K. Riesen and H. Bunke. Approximate graph edit distance computation by means of bipartite graph matching. Image and Vision Computing, 27(7):950--959, 2009.

Digital Library

[99]

K. Riesen, M. Ferrer, and H. Bunke. Approximate graph edit distance in quadratic time. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2015.

Digital Library

[100]

C. Romanowski, R. Nagi, and M. Sudit. Data mining in an engineering design environment: Or applications from graph matching. Computers & operations research, 33(11):3150--3160, 2006.

Digital Library

[101]

K. Sanders, B. Kettler, and J. Hendler. The case for graph-structured representations. In Case-Based Reasoning Research and Development, pages 245--254. 1997.

Digital Library

[102]

A. Sanfeliu and K.-S. Fu. A distance measure between attributed relational graphs for pattern recognition. pages 353--362, 1983.

[103]

G. Sanromà, R. Alquézar, and F. Serratosa. Attributed graph matching for image-features association using sift descriptors. In Structural, Syntactic, and Statistical Pattern Recognition, pages 254--263. 2010.

Digital Library

[104]

G. Sanromà, R. Alquézar, and F. Serratosa. Smooth simultaneous structural graph matching and point-set registration. In Graph-Based Representations in Pattern Recognition, pages 142--151. Springer, 2011.

Digital Library

[105]

G. Sanromà Güell, R. Alquézar Mancho, and F. Serratosa Casanelles. Graph matching using sift descriptors: an application to pose recovery of a mobile robot. In 5th International Conference on Computer Vision Theory and Applications, pages 249--254, 2010.

[106]

M. D. Santo, P. Foggia, C. Sansone, and M. Vento. A large database of graphs and its use for benchmarking graph isomorphism algorithms. Pattern Recognition Letters, 24(8):1067--1079, 2003.

Digital Library

[107]

C. Schellewald and C. Schnörr. Probabilistic subgraph matching based on convex relaxation. In EMMCVPR, 2005.

Digital Library

[108]

A. Schenker, M. Last, H. Bunke, and A. Kandel. Classification of web documents using graph matching. International Journal of Pattern Recognition and Artificial Intelligence, 18(03):475--496, 2004.

[109]

F. Serratosa. Fast computation of bipartite graph matching. Pattern Recognition Letters, 45:244--250, 2014.

[110]

F. Serratosa. Speeding up fast bipartite graph matching through a new cost matrix. International Journal of Pattern Recognition and Artificial Intelligence, 29(02):1550010, 2015.

[111]

C. Shen and W. Tsai. A graph matching approach to optimal task assignment in distributed computing systems using a minimax criterion. IEEE Transactions on Computers, 100(3):197--203, 1985.

Digital Library

[112]

D. Shen and C. D. Hammer. Hierarchical attribute matching mechanism for elastic registration. TMI, 2002.

[113]

Y. Shen, W. Lin, J. Yan, M. Xu, J. Wu, and J. Wang. Person re-identification with correspondence structure learning. In ICCV, 2015.

Digital Library

[114]

X. Shi, H. Ling, W. Hu, J. Xing, and Y. Zhang. Tensor power iteration for multi-graph matching. In CVPR, 2016.

[115]

A. Shokoufandeh, Y. Keselman, M. F. Demirci, D. Macrini, and S. Dickinson. Many-to-many feature matching in object recognition: a review of three approaches. Computer Vision, IET, 6(6):500--513, 2012.

[116]

P. Showbridge, M. Kraetzl, and D. Ray. Detection of abnormal change in dynamic networks. In Information, Decision and Control, pages 557--562, 1999.

[117]

A. Solé-Ribalta and F. Serratosa. Models and algorithms for computing the common labelling of a set of attributed graphs. CVIU, 2011.

Digital Library

[118]

A. Sole-Ribalta and F. Serratosa. Graduated assignment algorithm for multiple graph matching based on a common labeling. IJPRAI, 2013.

[119]

Y. Suh, M. Cho, and K. M. Lee. Graph matching via sequential monte carlo. In ECCV, 2012.

Digital Library

[120]

Y. Tian, R. Mceachin, C. Santos, and J. Patel. Saga: a subgraph matching tool for biological graphs. Bioinformatics, 2007.

Digital Library

[121]

Y. Tian, J. Yan, H. Zhang, Y. Zhang, X. Yang, and H. Zha. On the convergence of graph matching: Graduated assignment revisited. In ECCV, 2012.

Digital Library

[122]

P. H. S. Torr. Solving markov random fields using semidefinite programmin. In AISTATS, 2003.

[123]

L. Torresani, V. Kolmogorov, and C. Rother. Feature correspondence via graph matching: Models and global optimization. In ECCV, 2008.

Digital Library

[124]

S. Umeyama. An eigendecomposition approach to weighted graph matching problems. TPAMI, 1988.

Digital Library

[125]

M. Vento. A long trip in the charming world of graphs for pattern recognition. Pattern Recognition, 48(2):291--301, 2015.

Digital Library

[126]

M. Vento and P. Foggia. Graph matching techniques for computer vision. Graph-Based Methods in Computer Vision: Developments and Applications: Developments and Applications, page 1, 2012.

[127]

C. Wang, L. Wang, and L. Liu. Density maximization for improving graph matching with its applications. IEEE Transactions on Image Processing, 2015.

[128]

M. Wang, Y. Lai, Y. Liang, R. R. Martin, and S.-M. Hu. Biggerpicture: data-driven image extrapolation using graph matching. ACM Transactions on Graphics, 33(6), 2014.

Digital Library

[129]

W. Wang, W. Lin, Y. Chen, J. Wu, J. Wang, and B. Sheng. Finding coherent motions and semantic regions in crowd scenes: a diffusion and clustering approach. In ECCV, 2014.

[130]

M. L. Williams, R. C. Wilson, and E. Hancock. Multiple graph matching with bayesian inference. Pattern Recognition Letters, pages 1275--1281, 1997.

Digital Library

[131]

R. C. Wilson and E. R. Hancock. A bayesian compatibility model for graph matching. Pattern Recognition Letters, 17(3):263--276, 1996.

Digital Library

[132]

Q. Xiao, Y. Wang, and H. Wang. Motion retrieval using weighted graph matching. Soft Computing, 19(1), 2015.

Digital Library

[133]

J. Yan, M. Cho, H. Zha, X. Yang, and S. Chu. Multi-graph matching via affinity optimization with graduated consistency regularization. TPAMI, 2016.

[134]

J. Yan, Y. Li, W. Liu, H. Zha, X. Yang, and S. Chu. Graduated consistency-regularized optimization for multi-graph matching. In ECCV, 2014.

[135]

J. Yan, Y. Tian, H. Zha, X. Yang, Y. Zhang, and S. Chu. Joint optimization for consistent multiple graph matching. In ICCV, 2013.

Digital Library

[136]

J. Yan, J. Wang, H. Zha, and X. Yang. Consistency-driven alternating optimization for multigraph matching: A unified approach. IEEE Transactions on Image Processing, 2015.

Digital Library

[137]

J. Yan, J. Wang, H. Zha, X. Yang, and S. Chu. Multi-view point registration via alternating optimization. In AAAI, 2015.

Digital Library

[138]

J. Yan, H. Xu, H. Zha, X. Yang, H. Liu, and S. Chu. A matrix decomposition perspective to multiple graph matching. In ICCV, 2015.

Digital Library

[139]

J. Yan, C. Zhang, H. Zha, W. Liu, X. Yang, and S. Chu. Discrete hyper-graph matching. In CVPR, 2015.

[140]

Y. Yan, E. Ricci, R. Subramanian, G. Liu, O. Lanz, and N. Sebe. A multi-task learning framework for head pose estimation under target motion. 2016.

[141]

Q. Yang and S. Sze. Path matching and graph matching in biological networks. Journal Of Computational Biology, 2007.

[142]

B. Yao and F. Li. Action recognition with exemplar based 2.5 d graph matching. In ECCV, 2012.

Digital Library

[143]

C. Yu, L. Tian, H. Hu, Y. Duan, and J. Zhou. Progressive feature matching via triplet graph. In ICIP, 2015.

Digital Library

[144]

T. Yu and R. Wang. Scene parsing using graph matching on street-view data. CVIU, 2016.

Digital Library

[145]

M. Zaslavskiy, F. Bach, and J.-P. Vert. Many-to-many graph matching: a continuous relaxation approach. In Machine Learning and Knowledge Discovery in Databases, pages 515--530. Springer, 2010.

Digital Library

[146]

M. Zaslavskiy, F. R. Bach, and J.-P. Vert. Global alignment of protein-protein interaction networks by graph matching methods. Bioinformatics, 2009.

Digital Library

[147]

M. Zaslavskiy, F. R. Bach, and J.-P. Vert. A path following algorithm for the graph matching problem. TPAMI, 2009.

Digital Library

[148]

R. Zass and A. Shashua. Probabilistic graph and hypergraph matching. In CVPR, 2008.

[149]

D. Zhang and S. Chang. Detecting image near-duplicate by stochastic attributed relational graph matching with learning. In ACM MM, pages 877--884, 2004.

Digital Library

[150]

J. Zhang and P. Yu. Multiple anonymized social networks alignment. In ICDM, 2015.

Digital Library

[151]

Z. Zhang. Iterative point matching for registration of free-form curves and surfaces. IJCV, 1994.

Digital Library

[152]

W.-L. Zhao, C.-W. Ngo, H.-K. Tan, and X. Wu. Near-duplicate keyframe identification with interest point matching and pattern learning. IEEE Transactions on Multimedia, 9(5):1037--1048, 2007.

Digital Library

[153]

J. Zhong, H. Zhu, J. Li, and Y. Yu. Conceptual graph matching for semantic search. In Conceptual structures: Integration and interfaces, pages 92--106. 2002.

Digital Library

[154]

F. Zhou and F. Torre. Factorized graph matching. IEEE PAMI, 2016.

Digital Library

[155]

F. Zhou and F. D. Torre. Deformable graph matching. In CVPR, 2013.

Digital Library

[156]

J. Zhou, H. Yan, and Y. Zhu. Local topology preserved tensor models for graph matching. In IEEE International Conference on Systems, Man, and Cybernetics, pages 2153--2157, 2015.

Digital Library

[157]

K. Zhou, H. Zha, and L. Song. Learning triggering kernels for multi-dimensional hawkes processes. In ICML, pages 1301--1309, 2013.

Digital Library

[158]

X. Zhou, M. Zhu, and K. Daniilidis. Multi-image matching via fast alternating minimization. In ICCV, 2015.

Digital Library

[159]

H. Zhu, D. Liu, S. Zhang, Y. Zhu, L. Teng, and S. Teng. Solving the many to many assignment problem by improving the kuhn--munkres algorithm with backtracking. Theoretical Computer Science, 2016.

Digital Library

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Kim JLee HNguyen DShin MKwon BKo SElmqvist N(2025)DG Comics: Semi-Automatically Authoring Graph Comics for Dynamic GraphsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345634031:1(973-983)Online publication date: Jan-2025
https://doi.org/10.1109/TVCG.2024.3456340
Rocco CMoronta JBarker K(2024)TOPOLOGY CHANGE DETECTION IN NETWORKS DUE TO FALSE DATA INJECTIONS: A PRIORI ASSESSMENT BASED ON GRAPH MATCHING TECHNIQUESSSRN Electronic Journal10.2139/ssrn.4696774Online publication date: 2024
https://doi.org/10.2139/ssrn.4696774
Jin J(2024)Algorithms for graph theory matching problems based on computer scienceInternational Journal of Modern Physics C10.1142/S0129183124410055Online publication date: 4-Dec-2024
https://doi.org/10.1142/S0129183124410055
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Index Terms

A Short Survey of Recent Advances in Graph Matching
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Activity recognition and understanding

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cover image ACM Conferences

ICMR '16: Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval

June 2016

452 pages

ISBN:9781450343596

DOI:10.1145/2911996

General Chairs:
John R. Kender
Columbia University, USA
,
John R. Smith
IBM Research, USA
,
Program Chairs:
Jiebo Luo
University of Rochester, USA
,
Susanne Boll
University of Oldenburg, Germany
,
Winston Hsu
National Taiwan University, Taiwan

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 June 2016

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NSFC
Shanghai Science and Technology Commitee

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ICMR'16

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SIGMM

ICMR'16: International Conference on Multimedia Retrieval

June 6 - 9, 2016

New York, New York, USA

Acceptance Rates

ICMR '16 Paper Acceptance Rate 20 of 120 submissions, 17%;

Overall Acceptance Rate 254 of 830 submissions, 31%

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Cited By

Kim JLee HNguyen DShin MKwon BKo SElmqvist N(2025)DG Comics: Semi-Automatically Authoring Graph Comics for Dynamic GraphsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345634031:1(973-983)Online publication date: Jan-2025
https://doi.org/10.1109/TVCG.2024.3456340
Rocco CMoronta JBarker K(2024)TOPOLOGY CHANGE DETECTION IN NETWORKS DUE TO FALSE DATA INJECTIONS: A PRIORI ASSESSMENT BASED ON GRAPH MATCHING TECHNIQUESSSRN Electronic Journal10.2139/ssrn.4696774Online publication date: 2024
https://doi.org/10.2139/ssrn.4696774
Jin J(2024)Algorithms for graph theory matching problems based on computer scienceInternational Journal of Modern Physics C10.1142/S0129183124410055Online publication date: 4-Dec-2024
https://doi.org/10.1142/S0129183124410055
Zeng ZDong CWu IZhu XZhang L(2024)Optimal UAV Swarm Reconstruction Strategy Based on Minimum Cost Maximum Flow Algorithm2024 IEEE Wireless Communications and Networking Conference (WCNC)10.1109/WCNC57260.2024.10571021(01-06)Online publication date: 21-Apr-2024
https://doi.org/10.1109/WCNC57260.2024.10571021
Li ZJohnson BSussman DPriebe CLyzinski V(2024)Gotta Match 'Em All: Solution Diversification in Graph Matching Matched FiltersIEEE Transactions on Signal and Information Processing over Networks10.1109/TSIPN.2024.346792110(752-764)Online publication date: 2024
https://doi.org/10.1109/TSIPN.2024.3467921
Shin YKim SShin W(2024)PAGE: Prototype-Based Model-Level Explanations for Graph Neural NetworksIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.337925146:10(6559-6576)Online publication date: Oct-2024
https://doi.org/10.1109/TPAMI.2024.3379251
Yang JZhang XFan SRen CZhang Y(2024)Mutual Voting for Ranking 3D CorrespondencesIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2023.326829746:6(4041-4057)Online publication date: Jun-2024
https://doi.org/10.1109/TPAMI.2023.3268297
Hong XZha KFeng ZChen ZDu XLiu M(2024)DAFA: A Dual-Awareness Feature Aggregator for Table Structure Recognition on Medical Examination ReportsIEEE Access10.1109/ACCESS.2023.334632612(1321-1329)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2023.3346326
Yi BO’Connor KMcGoff KNobel A(2024)Alignment and comparison of directed networks via transition couplings of random walksJournal of the Royal Statistical Society Series B: Statistical Methodology10.1093/jrsssb/qkae085Online publication date: 10-Sep-2024
https://doi.org/10.1093/jrsssb/qkae085
Zhu XZhu LGeng X(2024) k ‐wise multi‐graph matching IET Image Processing10.1049/ipr2.1328518:14(4760-4777)Online publication date: 26-Nov-2024
https://doi.org/10.1049/ipr2.13285
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