A Hypergraph Matching Framework for Refining Multi-source Feature Correspondences

  • He ZhangEmail author
  • Bin Du
  • Yanjiang Wang
  • Peng Ren
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9069)


In this paper, we develop a hypergraph matching framework which enables feature correspondence refinement for multi-source images. For images obtained from different sources (e.g., RGB images and infrared images), we first extract feature points by using one feature extraction scheme. We then establish feature point correspondences in terms of feature similarities. In this scenario, mismatches tend to occur because the feature extraction scheme may exhibit certain ambiguity in characterizing feature similarities for multi-source images. To eliminate this ineffectiveness, we establish an association hypergraph based on the feature point correspondences, where one vertex represents a feature point pair resulted from the feature matching and one hyperedge reflects the higher-order structural similarity among feature point tuples. We then reject the mismatches by identifying outlier vertices of the hypergraph through higher order clustering. Our method is invariant to scale variation of objects because of its capability for characterizing higher order structure. Furthermore, our method is computationally more efficient than existing hypergraph matching methods because the feature matching heavily reduces the enumeration of possible point tuples for establishing hypergraph models. Experimental results show the effectiveness of our method for refining feature matching.


Hypergraph matching Feature matching Multi-source image processing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14, 239–256 (1992)CrossRefGoogle Scholar
  2. 2.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24, 509–522 (2002)CrossRefGoogle Scholar
  3. 3.
    Brown, M., Lowe, D.G.: Recognising panoramas. In: Internat. Conf. on Computer Vision (ICCV), vol. 2, pp. 1218–1225 (2003)Google Scholar
  4. 4.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 2, 91–110 (2004)CrossRefGoogle Scholar
  5. 5.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. Computer Vision and Image Understanding 110, 346–359 (2008)CrossRefGoogle Scholar
  6. 6.
    Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: Internat. Conf. on Computer Vision (ICCV), pp. 2564–2571 (2011)Google Scholar
  7. 7.
    Yan, Q., Shen, X., Xu, L., Zhuo, S., Zhang, X., Shen, L., Jia, J.: Cross-field joint image restoration via scale map. In: Internat. Conf. on Computer Vision (ICCV), pp. 1537–1544 (2013)Google Scholar
  8. 8.
    Lowe, D.G.: Object Recognition from Local Scale-Invariant Features. In: Internat. Conf. on Computer Vision (ICCV), vol. 2, pp. 1150–1157 (1999)Google Scholar
  9. 9.
    Ren, P., Wilson, R.C., Hancock, E.R.: High Order Structural Matching Using Dominant Cluster Analysis. In: Maino, G., Foresti, G.L. (eds.) ICIAP 2011, Part I. LNCS, vol. 6978, pp. 1–8. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    Duchenne, O., Bach, F., Kweon, I., Ponce, J.: A Tensor-Based Algorithm for High-Order Graph Matching. IEEE Trans. Pattern Anal. Mach. Intell. 33, 2383–2395 (2011)CrossRefGoogle Scholar
  11. 11.
    Conte, D., Foggia, P., Sansone, C., Vento, M.: Thirty Years of Graph Matching in Pattern Recognition. Int. J. Pattern Recognition and Artificial Intelligence 18, 265–298 (2004)CrossRefGoogle Scholar
  12. 12.
    Gong, M., Zhao, S., Jiao, L., Tian, D., Wang, S.: A novel coarse-to-fine scheme for automatic image registration based on SIFT and mutual information. IEEE Trans. Geoscience and Remote Sensing 52, 4328–4338 (2014)CrossRefGoogle Scholar
  13. 13.
    Lerman, G., Whitehouse, J.T.: On d-dimensional d-semimetrics and simplex-type inequalities for high-dimensional sine functions. J. Approximation Theory 156, 52–81 (2009)CrossRefzbMATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.College of Information and Control EngineeringChina University of PetroleumQingdaoChina

Personalised recommendations