Multi-view Matching for Unordered Image Sets, or “How Do I Organize My Holiday Snaps?”

  • F. Schaffalitzky
  • A. Zisserman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2350)


There has been considerable success in automated reconstruction for image sequences where small baseline algorithms can be used to establish matches across a number of images. In contrast in the case of widely separated views, methods have generally been restricted to two or three views.

In this paper we investigate the problem of establishing relative viewpoints given a large number of images where no ordering information is provided. A typical application would be where images are obtained from different sources or at different times: both the viewpoint (position, orientation, scale) and lighting conditions may vary significantly over the data set.

Such a problem is not fundamentally amenable to exhaustive pair wise and triplet wide baseline matching because this would be prohibitively expensive as the number of views increases. Instead, we investiate how a combination of image invariants, covariants, and multiple view relations can be used in concord to enable efficient multiple view matching. The result is a matching algorithm which is linear in the number of views.

The methods are illustrated on several real image data sets. The output enables an image based technique for navigating in a 3D scene, moving from one image to whichever image is the next most appropriate.


Span Tree Image Patch Invariant Vector Invariant Space Maximally Stable Extremal Region 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    S. Avidan and A. Shashua. Threading fundamental matrices. In Proc. ECCV, pages 124–140. Springer-Verlag, 1998.Google Scholar
  2. 2.
    A. Baumberg. Reliable feature matching across widely separated views. In Proc. CVPR, pages 774–781, 2000.Google Scholar
  3. 3.
    P. Beardsley, P. Torr, and A. Zisserman. 3D model acquisition from extended image sequences. In Proc. ECCV, LNCS 1064/1065, pages 683–695. Springer-Verlag, 1996.Google Scholar
  4. 4.
    A. W. Fitzgibbon and A. Zisserman. Automatic camera recovery for closed or open image sequences. In Proc. ECCV, pages 311–326. Springer-Verlag, Jun 1998.Google Scholar
  5. 5.
    R. Hartley, L. de Agapito, E. Hayman, and I. Reid. Camera calibration and the search for infinity. In Proc. ICCV, pages 510–517, September 1999.Google Scholar
  6. 6.
    R. I. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, ISBN: 0521623049, 2000.Google Scholar
  7. 7.
    R. Koch, M. Pollefeys, B. Heigl, L. Van Gool, and H. Niemann. Calibration of hand-held camera sequences for plenoptic modeling. In Proc. ICCV, pages 585–591, 1999.Google Scholar
  8. 8.
    T. Lindeberg and J. Gårding. Shape-adapted smoothing in estimation of 3-d depth cues from affine distortions of local 2-d brightness structure. In Proc. ECCV, pages 389–400, May 1994.Google Scholar
  9. 9.
    B. D. Lucas and T. Kanade. An iterative image registration technique with an application to stereo vision. In Proc. of the 7th International Joint Conference on Artificial Intelligence, pages 674–679, 1981.Google Scholar
  10. 10.
    J. Matas, J. Burianek, and J. Kittler. Object recognition using the invariant pixel-set signature. In Proc. BMVC., pages 606–615, 2000.Google Scholar
  11. 11.
    J. Matas, O. Chum, and T. Urban, M. an Pajdla. Distinguished regions for wide-baseline stereo. Research Report CTU-CMP-2001-33, Center for Machine Perception, K333 FEE Czech Technical University, Prague, Czech Republic, November 2001.Google Scholar
  12. 12.
    J Matas, M Urban, and T Pajdla. Unifying view for wide-baseline stereo. In B Likar, editor, Proc. Computer Vision Winter Workshop, pages 214–222, Ljubljana, Sloveni, February 2001. Slovenian Pattern Recorgnition Society.Google Scholar
  13. 13.
    K. Mikolajczyk and C. Schmid. Indexing based on scale invariant interest points. In Proc. ICCV, 2001.Google Scholar
  14. 14.
    K. Mikolajczyk and C. Schmid. An affine invariant interest point detector. In Proc. ECCV. Springer-Verlag, 2002.Google Scholar
  15. 15.
    P. Pritchett and A. Zisserman. Matching and reconstruction from widely separated views. In R. Koch and L. Van Gool, editors, 3D Structure from Multiple Images of Large-Scale Environments, LNCS 1506, pages 78–92. Springer-Verlag, Jun 1998.CrossRefGoogle Scholar
  16. 16.
    P. Pritchett and A. Zisserman. Wide baseline stereo matching. In Proc. ICCV, pages 754–760, Jan 1998.Google Scholar
  17. 17.
    H. S. Sawhney, S. Hsu, and R. Kumar. Robust video mosaicing through topology inference and local to global alignment. In Proc. ECCV, pages 103–119. Springer-Verlag, 1998.Google Scholar
  18. 18.
    F. Schaffalitzky and A. Zisserman. Viewpoint invariant texture matching and wide baseline stereo. In Proc. ICCV, Jul 2001.Google Scholar
  19. 19.
    F. Schaffalitzky, A. Zisserman, Hartley, R. I., and P. H. S. Torr. A six point solution for structure and motion. In Proc. ECCV, pages 632–648. Springer-Verlag, Jun 2000.Google Scholar
  20. 20.
    C. Schmid and R. Mohr. Local greyvalue invariants for image retrieval. IEEE PAMI, 19(5):530–534, May 1997.Google Scholar
  21. 21.
    D. Tell and S. Carlsson. Wide baseline point matching using affine invariants computed from intensity profiles. In Proc. ECCV. Springer-Verlag, Jun 2000.Google Scholar
  22. 22.
    W. Triggs, P. McLauchlan, R. Hartley, and A. Fitzgibbon. Bundle adjustment: A modern synthesis. In W. Triggs, A. Zisserman, and R. Szeliski, editors, Vision Algorithms: Theory and Practice, LNCS. Springer Verlag, 2000.Google Scholar
  23. 23.
    T. Tuytelaars and L. Van Gool. Content-based image retrieval based on local affinely invariant regions. In Int. Conf. on Visual Information Systems, pages 493–500, 1999.Google Scholar
  24. 24.
    T. Tuytelaars and L. Van Gool. Wide baseline stereo matching based on local, affinely invariant regions. In Proc. BMVC., pages 412–425, 2000.Google Scholar
  25. 25.
    Z. Zhang, R. Deriche, O. D. Faugeras, and Q.-T. Luong. A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artificial Intelligence, 78:87–119, 1995.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • F. Schaffalitzky
    • 1
  • A. Zisserman
    • 1
  1. 1.Robotics Research GroupUniversity of OxfordUK

Personalised recommendations