Kernel Spectral Correspondence Matching Using Label Consistency Constraints

  • Hongfang Wang
  • Edwin R. Hancock
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)


This paper investigates a kernel spectral approach to the problem of point pattern matching. Our first contribution is to show how kernel principal components analysis can be effectively used for solving the point correspondence matching problem when the point-sets are subject to structural errors, i.e. they are of different size. Our second contribution is to show how label consistency constraints can be incorporated into the construction of the Gram matrices for solving the articulated point pattern matching problem. We compare our algorithm with earlier point matching approaches and provide experiments on both synthetic data and real world data.


Feature Point Label Probability Kernel Principal Component Analysis Proximity Matrix Rigid Component 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Carcassoni, M., Hancock, E.R.: Spectral correspondence for point pattern matching. Pattern Recognition 36, 193–204 (2003)zbMATHCrossRefGoogle Scholar
  2. 2.
    Carcassoni, M., Hancock, E.R.: Correspondence matching with modal clusters. IEEE Tran. PAMI 25(12) (2003)Google Scholar
  3. 3.
    Chui, H., Rangarajan, A.: A new point matching algorithm for non-rigid registration. Computer Vision and Image Understanding 89, 114–141 (2003)zbMATHCrossRefGoogle Scholar
  4. 4.
    Chung, F.R.K.: Spectral Graph Theory. Amer. Math. Soc. 92 (1997)Google Scholar
  5. 5.
    Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Training Models of Shape from Sets of Examples. In: Proceedings BMVC, pp. 9–18 (1992)Google Scholar
  6. 6.
    Cox, T.F., Cox, M.A.A.: Multidimensional Scaling. Chapman and Hall, Boca Raton (1994)zbMATHGoogle Scholar
  7. 7.
    Kittler, J., Hancock, E.R.: Combining Evidence In Probabilistic Relaxation. Intern. Jour. Patt. Recog. And Arti. Intel. 3(1), 29–51 (1989)CrossRefGoogle Scholar
  8. 8.
    Pappu, S., Gold, S., Rangarajan, A.: A framework for non-rigid matching and correspondence. Advances in Neural Information Processing Systems 8 (1996)Google Scholar
  9. 9.
    Pelillo, M., Refice, M.: Learning Compatibility Coefficients for Relaxation Labeling Processes. IEEE Trans. PAMI 16(9), 933–945 (1994)Google Scholar
  10. 10.
    Pilu, M.: A direct method for stereo correspondence based on singular value decomposition. In: IEEE CVPR, pp. 261–266 (1997)Google Scholar
  11. 11.
    Rosenfeld, A., Hummel, R., Zucker, S.: Scene labeling by relaxation operations. IEEE Trans. Systems. Man and Cybernetics 6, 420–433 (1976)zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Schölkopf, B., Smola, A.J., Müller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10, 1299–1319 (1998)CrossRefGoogle Scholar
  13. 13.
    Scott, G.L., Longuet-Higgins, H.C.: An Algorithm for Associating the Features of Two Images. Proc. Royal Soc. London Series B 244, 21–26 (1991)CrossRefGoogle Scholar
  14. 14.
    Shapiro, L.S., Brady, J.M.: Feature-Based Correspondence - An Eigenvector Approach. Image and Vision Computing 10, 283–288 (1992)CrossRefGoogle Scholar
  15. 15.
    Tomasi, C., Kanade, T.: Shape and motion from image streams under orthography – A factorization method. Tech. Rept TR-92-1270, Cornell University (1992)Google Scholar
  16. 16.
    Vapnik, V.N.: Statistical learning theory. John Wiley & Sons, Inc., Chichester (1998)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Hongfang Wang
    • 1
  • Edwin R. Hancock
    • 1
  1. 1.Dept. of Computer ScienceUniversity of YorkHeslington, YorkUK

Personalised recommendations