Abstract
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.
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© 2009 Springer-Verlag Berlin Heidelberg
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Knossow, D., Sharma, A., Mateus, D., Horaud, R. (2009). Inexact Matching of Large and Sparse Graphs Using Laplacian Eigenvectors. In: Torsello, A., Escolano, F., Brun, L. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2009. Lecture Notes in Computer Science, vol 5534. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02124-4_15
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DOI: https://doi.org/10.1007/978-3-642-02124-4_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02123-7
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