Abstract
This paper describes a new eigendecomposition method for weighted graph-matching. Although elegant by means of its matrix representation, the eigendecomposition method is proved notoriously susceptible to differences in the size of the graphs under consideration. In this paper we demonstrate how the method can be rendered robust to structural differences by adopting a hierarchical approach. We place the weighted graph matching problem in a probabilistic setting in which the correspondences between pairwise clusters can be used to constrain the individual correspondences. By assigning nodes to pairwise relational clusters, we compute within-cluster and between-cluster adjacency matrices. The modal co-efficients for these adjacency matrices are used to compute cluster correspondence and cluster-conditional correspondence probabilities. A sensitivity study on synthetic point-sets reveals that the method is considerably more robust than the conventional method to clutter or point-set contamination.
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References
Chung, F.R.K.: Spectral Graph Theory. CBMS series 92. AMS Ed. (1997)
Carcassoni, M., Hancock, E.R.: Point Pattern Matching with Robust Spectral Correspondence. CVPR (2000)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum-likelihood from incomplete data via the EM algorithm. J. Royal Statistical Soc. Ser. B (methodological) 39 (1977) 1–38
Horaud, R., Sossa, H.: Polyhedral Object Recognition by Indexing. Pattern Recognition 28 (1995) 1855–1870
Inoue, K., Urahama, K.: Sequential fuzzy cluster extraction by a graph spectral method. Pattern Recognition Letters, 20 (1999) 699–705
Mokhtarian, F., Suomela, R.: Robust Image Corner Detection Through Curvature Scale Space. IEEE PAMI, 20 (December 1998) 1376–1381
Perona, P., Freeman, W.: A Factorisation Approach to Grouping. ECCV 98, Vol 1 (1998) 655–670
Scott, G.L., Longuet-Higgins, H.C.: An algorithm for associating the features of 2 images. In: Proceedings of the Royal Society of London Series B (Biological) 244 (1991) 21–26
Sengupta, K., Boyer, K.L.: Modelbase partitioning using property matrix spectra. Computer Vision and Image Understanding, 70 (1998) 177–196
Shokoufandeh, A., Dickinson, S.J., Siddiqi, K., Zucker, S.W.: Indexing using a spectral encoding of topological structure. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (1999) 491–497
Shapiro, L.S., Brady, J.M.: Feature-based correspondence-an eigenvector approach. Image and Vision Computing, 10 (1992) 283–288
Shi, J., Malik, J.: Normalized cuts and image segmentation. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (1997)
Umeyama, S.: An eigen decomposition approach to weighted graph matching problems. IEEE PAMI 10 (1988) 695–703
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© 2001 Springer-Verlag Berlin Heidelberg
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Carcassoni, M., Hancock, E.R. (2001). Weighted Graph-Matching Using Modal Clusters. In: Skarbek, W. (eds) Computer Analysis of Images and Patterns. CAIP 2001. Lecture Notes in Computer Science, vol 2124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44692-3_18
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DOI: https://doi.org/10.1007/3-540-44692-3_18
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42513-7
Online ISBN: 978-3-540-44692-7
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