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Density maximization for improving graph matching with its applications

Journal Article


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Abstract


  • Graph matching has been widely used in both image

    processing and computer vision domain due to its powerful

    performance for structural pattern representation. However,

    it poses three challenges to image sparse feature matching:

    1) the combinatorial nature limits the size of the possible

    matches; 2) it is sensitive to outliers because its objective function

    prefers more matches; and 3) it works poorly when handling

    many-to-many object correspondences, due to its assumption of

    one single cluster of true matches. In this paper, we address

    these challenges with a unified framework called density

    maximization (DM), which maximizes the values of a proposed

    graph density estimator both locally and globally. DM leads

    to the integration of feature matching, outlier elimination, and

    cluster detection. Experimental evaluation demonstrates that

    it significantly boosts the true matches and enables graph

    matching to handle both outliers and many-to-many object

    correspondences. We also extend it to dense correspondence

    estimation and obtain large improvement over the state-of-the-art

    methods. We further demonstrate the usefulness of our methods

    using three applications: 1) instance-level image retrieval;

    2) mask transfer; and 3) image enhancement.

Authors


  •   Wang, Chao (external author)
  •   Wang, Lei
  •   Liu, Lingqiao (external author)

Publication Date


  • 2015

Citation


  • Wang, C., Wang, L. & Liu, L. (2015). Density maximization for improving graph matching with its applications. IEEE Transactions on Image Processing, 24 (7), 2110-2123.

Scopus Eid


  • 2-s2.0-84927723745

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=4613&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/3594

Has Global Citation Frequency


Number Of Pages


  • 13

Start Page


  • 2110

End Page


  • 2123

Volume


  • 24

Issue


  • 7

Place Of Publication


  • United States

Abstract


  • Graph matching has been widely used in both image

    processing and computer vision domain due to its powerful

    performance for structural pattern representation. However,

    it poses three challenges to image sparse feature matching:

    1) the combinatorial nature limits the size of the possible

    matches; 2) it is sensitive to outliers because its objective function

    prefers more matches; and 3) it works poorly when handling

    many-to-many object correspondences, due to its assumption of

    one single cluster of true matches. In this paper, we address

    these challenges with a unified framework called density

    maximization (DM), which maximizes the values of a proposed

    graph density estimator both locally and globally. DM leads

    to the integration of feature matching, outlier elimination, and

    cluster detection. Experimental evaluation demonstrates that

    it significantly boosts the true matches and enables graph

    matching to handle both outliers and many-to-many object

    correspondences. We also extend it to dense correspondence

    estimation and obtain large improvement over the state-of-the-art

    methods. We further demonstrate the usefulness of our methods

    using three applications: 1) instance-level image retrieval;

    2) mask transfer; and 3) image enhancement.

Authors


  •   Wang, Chao (external author)
  •   Wang, Lei
  •   Liu, Lingqiao (external author)

Publication Date


  • 2015

Citation


  • Wang, C., Wang, L. & Liu, L. (2015). Density maximization for improving graph matching with its applications. IEEE Transactions on Image Processing, 24 (7), 2110-2123.

Scopus Eid


  • 2-s2.0-84927723745

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=4613&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/3594

Has Global Citation Frequency


Number Of Pages


  • 13

Start Page


  • 2110

End Page


  • 2123

Volume


  • 24

Issue


  • 7

Place Of Publication


  • United States