Bridging structure and feature representations in graph matching

International Journal of Pattern Recognition and Artificial Intelligence

Volumn 26, Issue 5, 2012, Pages

  Lee, Wan Jui ^a   Cheplygina, Veronika ^a   Tax, David M J ^a   Loog, Marco ^a   Duin, Robert P W ^a  

^a DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

Graph edit distance; graph kernel; multiple instance learning

Indexed keywords

OBJECT RECOGNITION;

CLASSIFICATION PERFORMANCE; DISSIMILARITY REPRESENTATIONS; ESSENTIAL PROBLEMS; FEATURE REPRESENTATION; GRAPH EDIT DISTANCE; GRAPH KERNELS; MULTIPLE INSTANCE LEARNING; STRUCTURAL DESCRIPTIONS;

PATTERN MATCHING;

EID: 84870535292     PISSN: 02180014     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218001412600051     Document Type: Article

References (51)

1. S. Andrews, T. Hofmann and I. Tsochantaridis, Multiple instance learning with generalized support vector machines, in Proc. AAAI National Conf. Artificial Intelligence (Alberta, Canada, 2002), pp. 943-944.
2. S. Andrews, I. Tsochantaridis and T. Hofmann, Support vector machines for multipleinstance learning, in Proc. Conf. Advances in Neural Information Processing Systems (2002), pp. 561-568.
3. M. Basu, H. Bunke and A. D. Bimbo (eds.), Syntactic and structural pattern recognition, Special Issue on IEEE Trans. Pattern Anal. Mach. Intell. 7(27) (2005).
4. K. Borgwardt and H. Kriegel, Shortest-ath kernels on graphs, in Proc. of ICDM'05, Houston, USA (IEEE Computer Society, 2005), pp. 74-81.
5. K. Borgwardt, C. Ong, S. Schonauer, S. Vishwanathan, A. Smola and H. Kriegel, Protein function prediction via graph kernels, Bioinformatics 21 (2005) i47-i56. (Pubitemid 41794474)
6. H. Bunke and G. Allermann, Inexact graph matching for structural pattern recognition, Pattern Recogn. Lett. 1 (1983) 245-253.
7. H. Bunke and T. Caelli (eds.), Graph matching in pattern recognition and machine vision, Special Issue on Int. J. Pattern Recogn. Artif. Intell. 18(3) (Springer, 2004).
8. H. Bunke and K. Riesen, Graph classification based on dissimilarity space embedding, in Structural, Syntactic, and Statistical Pattern Recognition, Lecture Notes in Computer Science, Vol. 5342 (Springer, 2008), pp. 996-1007.
9. H. Bunke and K. Riesen, Recent advances in graph-based pattern recognition with applications in document analysis, Pattern Recogn. 44(5) (2011) 1057-1067.
10. H. Bunke and A. Sanfeliu, Syntactic and Structural Pattern Recognition: Theory and Applications (World Scientific, Singapore, 1990).
11. T. Caelli and S. Kosinov, An eigenspace projection clustering method for inexact graph matching, IEEE Trans. Pattern Anal. Mach. Intell. 26 (2004) 515-519.
12. Y. Chen, J. Bi and J. Wang, Miles: Multiple-instance learning via embedded instance selection, IEEE Trans. Pattern Anal. Mach. Intell. 28(12) (2006) 1931-1947. (Pubitemid 46405705)
13. F. Chung, Spectral Graph Theory, CBMS Regional Conference Series in Mathematics, Vol. 92 (AMS, USA, 1997).
14. T. Cover and P. Hart, Nearest neighbor pattern classification, IEEE Trans. Inf. Theory 13(1) (1967) 21-27.
15. P. Devijver and J. Kittler, Pattern Recognition: A Statistical Approach. (Prentice-Hall, London, 1982).
16. T. Dietterich, R. Lathrop and T. Lozano-P-erez, Solving the multiple instance problem with axis-arallel rectangles, Artif. Intell. 89(1-2) (1997) 31-41.
17. R. Duda, P. Hart and D. Stork, Pattern Classification (John Wiley and Sons, New York, 2001).
18. R. Duin and E. Pekalska, Dis-Tools a matlab toolbox for pattern recognition (2009), http://prtools.org. 19.
19. R. Duin, P. Juszczak, P. Paclik, E. Pekalska, D. de Ridder, D. Tax and S. Verzakov, PRTools4.1, a matlab toolbox for pattern recognition (2007), http://prtools.org.
20. T. Gartner, Predictive graph mining with kernel methods, in Advanced Methods for Knowledge Discovery from Complex Data (Springer, 2005), pp. 95-121.
21. T. Gärtner, P. Flach, A. Kowwalczyk and A. Smola, Multi-instance kernels, in Proc. 19th Int. Conf. Machine Learning, eds. C. Sammut and A. Homann (Morgan Kaufmann, 2002), pp. 179-186.
22. T. Gartner, P. Flach and S. Wrobel, On graph kernels: Hardness results and eficient alternatives, in Proc. of COLT/Kernel'03, Washington DC, USA (Springer-Verlag, 2003), pp. 129-143. (Pubitemid 37053200)
23. C. Harris and M. Stephens, A combined corner and edge detector, in Proc. 4th Alvey Vision Conf. (1988), pp. 147-151.
24. P. Hart, N. Nilson and B. Raphael, A formal basis for the heuristic determination of minimum cost paths, IEEE Trans. Syst. Sci. Cybern. 4(2) (1968) 100-107.
25. T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning (Springer- Verlag, New York, 2001).
26. T. Horv-ath, T. Gärtner and S. Wrobel, Cyclic pattern kernels for predictive graph mining, in Proc. of KDD'04 (ACM, 2004), pp. 158-167. (Pubitemid 40114925)
27. A. Jain, R. Duin and J. Mao, Statistical pattern recognition: A review, IEEE Trans. Pattern Anal. Mach. Intell. 22(1) (2000) 4-7.
28. A. Kandel, H. Bunke and M. Last, Applied Graph Theory in Computer Vision and Pattern Recognition, Studies in Computational Intelligence, Vol. 52 (Springer, 2007).
29. H. Kashima, K. Tsuda and A. Inokuchi, Marginalized kernels between labeled graphs, in Proc. ICML'03, Vol. 20 (2003), p. 321.
30. W. Lee and R. Duin, An inexact graph comparison approach in joint eigenspace, in Structural, Syntactic, and Statistical Pattern Recognition, Lecture Notes in Computer Science, Vol. 5342 (2008), pp. 35-44.
31. D. G. Lowe, Distinctive image features from scale-invariant keypoints, Int. J. Comput. Vis. 60(2) (2004) 91-110.
32. O. Maron and T. Lozano-P-erez, A framework for multiple-instance learning, in Proc. Conf. Advances in Neural Information Processing Systems (1998), pp. 570-576.
33. J. Munkres, Algorithm for the assignment and transportation problems, J. Soc. Ind. Appl. Math. 5 (1957) 32-38.
34. S. Nene, S. Nayar and H. Murase, Columbia object image liberary: Coil-100, Tech. rep., Department of Computer Science, Columbia University, New York (1996).
35. E. Pekalska and R. Duin, Dissimilarity representations allow for building good classifiers, Pattern Recogn. Lett. 23(8) (2002) 943-956. (Pubitemid 34416229)
36. E. Pekalska and R. P. W. Duin, The Dissimilarity Representation for Pattern Recognition. Fundations and Applications (World Scientific, Singapore, 2005).
37. E. Pekalska, P. Paclik and R. Duin, A generalized kernel approach to dissimilarity-based classification, J. Mach. Learn. Res. 2 (2002) 175-211.
38. H. Qiu and E. Hancock, Clustering and embedding using commute times, IEEE Trans. Patt. Anal. Mach. Intell. 29 (2007) 1873-1890. (Pubitemid 47423784)
39. J. Ramon and T. Gärtner, Expressivity versus e±ciency of graph kernels, First International Workshop on Mining Graphs, Trees and Sequences, Citeseer (2003), pp. 65-74.
40. K. Riesen and H. Bunke, Approximate graph edit distance computation by means of bipartite graph matching, Image Vis. Comput. 27(7) (2009) 950-959.
41. K. Riesen and H. Bunke, Cluster ensembles based on vector space embeddings of graphs, in MCS (2009), pp. 211-221.
42. K. Riesen and H. Bunke, Feature ranking algorithms for improving classification of vector space embedded graphs, in CAIP (2009), pp. 377-384.
43. K. Riesen and H. Bunke, Graph classification based on vector space embedding, Int. J. Patt. Recogn. Artif. Intell. 23(6) (2009) 1053-1081.
44. K. Riesen and H. Bunke, IAM graph database repository for graph based pattern recognition and machine learning, Structural, Syntactic, and Statistical Pattern Recognition (Springer, 2010), pp. 287-297.
45. K. Riesen, M. Neuhaus and H. Bunke, Bipartite graph matching for computing the edit distance of graphs, in GbRPR (2007), pp. 1-12.
46. K. Riesen, S. Fankhauser, H. Bunke and P. J. Dickinson, E±cient suboptimal graph isomorphism, in GbRPR (2009), pp. 124-133.
47. Q. Tao, S. Scott, N. Vinodchandran, T. Osugi and B. Mueller, Kernels for generalized multiple-instance learning, IEEE Trans. Patt. Anal. Mach. Intell. 30(12) (2008) 2084-2098.
48. V. Vapnik, The Nature of Statistical Learning Theory (Springer-Verlag, New York, 1995).
49. P. Viola, J. Platt and C. Zhang, Multiple instance boosting for object detection, in Advances in Neural Inf. Proc. Systems (NIPS 05) (2005), pp. 1419-1426.
50. J. Wang and J. Zucker, Solving the multiple-instance problem: A lazy learning approach, in Proc. Thirteenth Int. Conf. Machine Learning (2000), pp. 1119-1126.
51. R. Wilson, B. Luo and E. Hancock, Pattern vectors from algebraic graph theory, IEEE Trans. Patt. Anal. Mach. Intell. 27 (2005) 1112-1124. (Pubitemid 41046906)