Graph matching with a dual-step EM algorithm Export

@article{Cross98, abstract = {This paper describes a new approach to matching geometric structure in 2D point-sets. The novel feature is to unify the tasks of estimating transformation geometry and identifying point-correspondence matches. Unification is realized by constructing a mixture model over the bipartite graph representing the correspondence match and by affecting optimization using the EM algorithm. According to our EM framework, the probabilities of structural correspondence gate contributions to the expected likelihood function used to estimate maximum likelihood transformation parameters. These gating probabilities measure the consistency of the matched neighborhoods in the graphs. The recovery of transformational geometry and hard correspondence matches are interleaved and are realized by applying coupled update operations to the expected log-likelihood function. We evaluate the technique on two real-world problems}, author = {Cross, A. D. J. and Hancock, E. R.}, booktitle = {Transactions on Pattern Analysis and Machine Intelligence}, citeulike-article-id = {2284897}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/34.730557}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=730557}, doi = {10.1109/34.730557}, journal = {Transactions on Pattern Analysis and Machine Intelligence}, keywords = {2d-imaging, graph-matching}, number = {11}, pages = {1236--1253}, posted-at = {2008-01-24 14:37:50}, priority = {2}, title = {Graph matching with a dual-step EM algorithm}, url = {http://dx.doi.org/10.1109/34.730557}, volume = {20}, year = {1998} }

TY - JOUR ID - Cross98 L3 - citeulike-article-id:2284897 N2 - This paper describes a new approach to matching geometric structure in 2D point-sets. The novel feature is to unify the tasks of estimating transformation geometry and identifying point-correspondence matches. Unification is realized by constructing a mixture model over the bipartite graph representing the correspondence match and by affecting optimization using the EM algorithm. According to our EM framework, the probabilities of structural correspondence gate contributions to the expected likelihood function used to estimate maximum likelihood transformation parameters. These gating probabilities measure the consistency of the matched neighborhoods in the graphs. The recovery of transformational geometry and hard correspondence matches are interleaved and are realized by applying coupled update operations to the expected log-likelihood function. We evaluate the technique on two real-world problems IS - 11 JF - Transactions on Pattern Analysis and Machine Intelligence EP - 1253 TI - Graph matching with a dual-step EM algorithm VL - 20 T2 - Transactions on Pattern Analysis and Machine Intelligence SP - 1236 KW - 2d-imaging KW - graph-matching AU - Cross, ADJ AU - Hancock, ER PY - 1998/// UR - http://dx.doi.org/10.1109/34.730557 ER -