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Progressive Probabilistic Graph Matching with Local Consistency Regularization

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Computer Analysis of Images and Patterns (CAIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10425))

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Abstract

Graph matching has attracted extensive attention in computer vision due to its powerful representation and robustness. However, its combinatorial nature and computational complexity limit the size of input graphs. Most graph matching methods initially reconstruct the graphs, while the preprocessing often results in poor performance. In this paper, a novel progressive probabilistic model is proposed in order to handle the outliers and boost the performance. This model takes advantage of the cooperation between process of correspondence enrichment and graph matching. Candidate matches are propagated with local consistency regularization in a probabilistic manner, and unreliable ones are rejected by graph matching. Experiments on two challenging datasets demonstrate that the proposed model outperforms the state-of-the-art progressive method in challenging real-world matching tasks.

This project was supported by Shenzhen Peacock Plan.

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Notes

  1. 1.

    Note that in this paper we use \(\texttt {x}_{ij}\) to denote \(\texttt {x}_{(i-1){n_2}+j}\).

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Authors and Affiliations

  1. School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University, Shenzhen, China

    Min Tang & Wenmin Wang

Authors
  1. Min Tang
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  2. Wenmin Wang
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Corresponding author

Correspondence to Wenmin Wang .

Editor information

Editors and Affiliations

  1. Linköping University, Linköping, Sweden

    Michael Felsberg

  2. Lund University, Lund, Sweden

    Anders Heyden

  3. University of Southern Denmark, Odense, Denmark

    Norbert Krüger

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Tang, M., Wang, W. (2017). Progressive Probabilistic Graph Matching with Local Consistency Regularization. In: Felsberg, M., Heyden, A., Krüger, N. (eds) Computer Analysis of Images and Patterns. CAIP 2017. Lecture Notes in Computer Science(), vol 10425. Springer, Cham. https://doi.org/10.1007/978-3-319-64698-5_10

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  • DOI: https://doi.org/10.1007/978-3-319-64698-5_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64697-8

  • Online ISBN: 978-3-319-64698-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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