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GMWASC: Graph matching with weighted affine and sparse constraints

Taheri Dezaki , F ; Sharif University of Technology | 2015

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  1. Type of Document: Article
  2. DOI: 10.1109/CSICSSE.2015.7369249
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2015
  4. Abstract:
  5. Graph Matching (GM) plays an essential role in computer vision and machine learning. The ability of using pairwise agreement in GM makes it a powerful approach in feature matching. In this paper, a new formulation is proposed which is more robust when it faces with outlier points. We add weights to the one-to-one constraints, and modify them in the process of optimization in order to diminish the effect of outlier points in the matching procedure. We execute our proposed method on different real and synthetic databases to show both robustness and accuracy in contrast to several conventional GM methods
  6. Keywords:
  7. Artificial intelligence ; Learning systems ; Software engineering ; Statistics ; Feature matching ; Graph matchings ; Synthetic database ; Computer vision
  8. Source: CSSE 2015 - 20th International Symposium on Computer Science and Software Engineering, 18 August 2015 ; 2015 ; 9781467391818 (ISBN)
  9. URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7369249