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GBRPR
2009
Springer

Inexact Matching of Large and Sparse Graphs Using Laplacian Eigenvectors

10 years 6 months ago
Inexact Matching of Large and Sparse Graphs Using Laplacian Eigenvectors
In this paper we propose an inexact spectral matching algorithm that embeds large graphs on a low-dimensional isometric space spanned by a set of eigenvectors of the graph Laplacian. Given two sets of eigenvectors that correspond to the smallest non-null eigenvalues of the Laplacian matrices of two graphs, we project each graph onto its eigenenvectors. We estimate the histograms of these one-dimensional graph projections (eigenvector histograms) and we show that these histograms are well suited for selecting a subset of significant eigenvectors, for ordering them, for solving the sign-ambiguity of eigenvector computation, and for aligning two embeddings. This results in an inexact graph matching solution that can be improved using a rigid point registration algorithm. We apply the proposed methodology to match surfaces represented by meshes.
David Knossow, Avinash Sharma, Diana Mateus, Radu
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where GBRPR
Authors David Knossow, Avinash Sharma, Diana Mateus, Radu Horaud
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