An Eigendecomposition Approach to Weighted Graph Matching Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature-based correspondence: an eigenvector approach
Image and Vision Computing - Special issue: BMVC 1991
A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Matching by Discrete Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Algorithm for Error-Tolerant Subgraph Isomorphism Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Spectral Algorithm for Seriation and the Consecutive Ones Problem
SIAM Journal on Computing
An energy function and continuous edit process for graph matching
Neural Computation
The String-to-String Correction Problem
Journal of the ACM (JACM)
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
A Graph Clustering Algorithm Based on Minimum and Normalized Cut
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
Attributed relational graph matching based on the nested assignment structure
Pattern Recognition
Spectral edit distance method for image clustering
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
3D CAD model search: a regularized manifold learning approach
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
From region based image representation to object discovery and recognition
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
TRACEMIN-Fiedler: a parallel algorithm for computing the Fiedler vector
VECPAR'10 Proceedings of the 9th international conference on High performance computing for computational science
Learning graph prototypes for shape recognition
Computer Vision and Image Understanding
High efficiency and quality: large graphs matching
Proceedings of the 20th ACM international conference on Information and knowledge management
3D CAD model retrieval with perturbed Laplacian spectra
Computers in Industry
Computer Science Review
Coloring based approach for matching unrooted and/or unordered trees
Pattern Recognition Letters
High efficiency and quality: large graphs matching
The VLDB Journal — The International Journal on Very Large Data Bases
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Although inexact graph-matching is a problem of potentially exponential complexity, the problem may be simplified by decomposing the graphs to be matched into smaller subgraphs. If this is done, then the process may cast into a hierarchical framework and hence rendered suitable for parallel computation. In this paper we describe a spectral method which can be used to partition graphs into non-overlapping subgraphs. In particular, we demonstrate how the Fiedler-vector of the Laplacian matrix can be used to decompose graphs into non-overlapping neighbourhoods that can be used for the purposes of both matching and clustering.