NDLI: A robust eigendecomposition framework for inexact graph-matching

Content Provider	IEEE Xplore Digital Library
Author	Luo, B. Hancock, E.R.
Copyright Year	2001
Description	Author affiliation: Dept. of Comput. Sci., York Univ., UK (Luo, B.)
Abstract	Graph-matching is a task of pivotal importance in high-level vision since it provides a means by which abstract pictorial descriptions can be matched to one another. This paper describes an efficient algorithm for inexact graph-matching. The method is purely structural, that is to say it uses only the edge or connectivity structure of the graph and does not draw on node or edge attributes. We make two contributions. Commencing from a probability distribution for matching errors, we show how the problem of graph-matching can be posed as maximum likelihood estimation using the apparatus of the EM algorithm. Our second contribution is to cast the recovery of correspondence matches between the graph nodes in a matrix framework. This allows us to efficiently recover correspondence matches using singular value decomposition. We experiment with the method on both real-world and synthetic data. Here we demonstrate that the method offers comparable performance to more computationally demanding methods.
Sponsorship	Int. Assoc. Pattern Recognition (IAPR)
Starting Page	465
Ending Page	470
File Size	599474
Page Count	6
File Format	PDF
ISBN	076951183X
DOI	10.1109/ICIAP.2001.957053
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-09-26
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robustness Probability distribution Matrix decomposition Singular value decomposition Graph theory Computer science Computer vision Image segmentation Entropy Energy measurement
Content Type	Text
Resource Type	Article

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