A Spectral Technique for Correspondence Problems using Pairwise Constraints

Marius Leordeanu and Martial Hebert
International Conference of Computer Vision (ICCV), October, 2005, pp. 1482 - 1489.


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Abstract
We present an efficient spectral method for finding consistent correspondences between two sets of features. We build the adjacency matrix M of a graph whose nodes represent the potential correspondences and the weights on the links represent pairwise agreements between potential correspondences. Correct assignments are likely to establish links among each other and thus form a strongly connected cluster. Incorrect correspondences establish links with the other correspondences only accidentally, so they are unlikely to belong to strongly connected clusters. We recover the correct assignments based on how strongly they belong to the main cluster of M, by using the principal eigenvector of M and imposing the mapping constraints required by the overall correspondence mapping (one-to-one or one-to-many). The experimental evaluation shows that our method is robust to outliers, accurate in terms of matching rate, while being several orders of magnitude faster than existing methods.

Keywords
matching, correspondence, object recognition, spectral method

Notes
Associated Center(s) / Consortia: Vision and Autonomous Systems Center
Number of pages: 8

Text Reference
Marius Leordeanu and Martial Hebert, "A Spectral Technique for Correspondence Problems using Pairwise Constraints," International Conference of Computer Vision (ICCV), October, 2005, pp. 1482 - 1489.

BibTeX Reference
@inproceedings{Leordeanu_2005_5161,
   author = "Marius Leordeanu and Martial Hebert",
   title = "A Spectral Technique for Correspondence Problems using Pairwise Constraints",
   booktitle = "International Conference of Computer Vision (ICCV)",
   pages = "1482 - 1489",
   month = "October",
   year = "2005",
   volume = "2",
}