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Improving graph matching via density maximization

Conference Paper


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Abstract


  • Graph matching has been widely used in various applications in computer vision due to its powerful performance. 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 the objective function prefers more matches, (3) It works poorly when handling many-to-many object correspondences, due to its assumption of one single cluster for each graph. In this paper, we address these problems with a unified framework-Density Maximization. We propose a graph density local estimator (DLE) to measure the quality of matches. Density Maximization aims to maximize the DLE values both locally and globally. The local maximization of DLE finds the clusters of nodes as well as eliminates the outliers. The global maximization of DLE efficiently refines the matches by exploring a much larger matching space. Our Density Maximization is orthogonal to specific graph matching algorithms. Experimental evaluation demonstrates that it significantly boosts the true matches and enables graph matching to handle both outliers and many-to-many object correspondences.

Authors


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

Publication Date


  • 2013

Citation


  • Wang, C., Wang, L. & Liu, L. (2013). Improving graph matching via density maximization. IEEE International Conference on Computer Vision (pp. 3424-3431). United States: Institute of Electrical and Electronics Engineers, Inc.

Scopus Eid


  • 2-s2.0-84898817782

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=3360&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/2351

Has Global Citation Frequency


Start Page


  • 3424

End Page


  • 3431

Place Of Publication


  • United States

Abstract


  • Graph matching has been widely used in various applications in computer vision due to its powerful performance. 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 the objective function prefers more matches, (3) It works poorly when handling many-to-many object correspondences, due to its assumption of one single cluster for each graph. In this paper, we address these problems with a unified framework-Density Maximization. We propose a graph density local estimator (DLE) to measure the quality of matches. Density Maximization aims to maximize the DLE values both locally and globally. The local maximization of DLE finds the clusters of nodes as well as eliminates the outliers. The global maximization of DLE efficiently refines the matches by exploring a much larger matching space. Our Density Maximization is orthogonal to specific graph matching algorithms. Experimental evaluation demonstrates that it significantly boosts the true matches and enables graph matching to handle both outliers and many-to-many object correspondences.

Authors


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

Publication Date


  • 2013

Citation


  • Wang, C., Wang, L. & Liu, L. (2013). Improving graph matching via density maximization. IEEE International Conference on Computer Vision (pp. 3424-3431). United States: Institute of Electrical and Electronics Engineers, Inc.

Scopus Eid


  • 2-s2.0-84898817782

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=3360&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/2351

Has Global Citation Frequency


Start Page


  • 3424

End Page


  • 3431

Place Of Publication


  • United States