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Published Articles >> Table of Contents >> Abstract
17th International Conference on Pattern Recognition (ICPR'04) - Volume 2
pp. 124-127
A Comparison of Junction Tree and Relaxation Algorithms for Point Matching using Different Distance Metrics
Caetano Caetano, University of Alberta, Canada; UFRGS, Brazil
Terry Caelli, University of Alberta, Canada
Dante A. C. Barone, UFRGS, Brazil
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPR.2004.1334077
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| Abstract |
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We have developed a polynomial time optimal method for a class of attributed graph matching problems using the Junction Tree algorithm from Graphical Models. In this paper we compare this method with standard probabilistic relaxation labelling using different forms of point metrics and under different levels of additive noise. Results show that, no matter which of the metrics is applied, our technique is more effective than probabilistic relaxation labeling for large graph sizes. For small graph sizes, our technique is still preferable for two of the metrics, while for the third one both techniques perform similarly.
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Citation:
Caetano Caetano, Terry Caelli, Dante A. C. Barone,
"A Comparison of Junction Tree and Relaxation Algorithms for Point Matching using Different Distance Metrics,"
icpr,
pp. 124-127,
17th International Conference on Pattern Recognition (ICPR'04) - Volume 2,
2004
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