SIFT Flow: Dense Correspondence across Different Scenes

  • Ce Liu
  • Jenny Yuen
  • Antonio Torralba
  • Josef Sivic
  • William T. Freeman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5304)


While image registration has been studied in different areas of computer vision, aligning images depicting different scenes remains a challenging problem, closer to recognition than to image matching. Analogous to optical flow, where an image is aligned to its temporally adjacent frame, we propose SIFT flow, a method to align an image to its neighbors in a large image collection consisting of a variety of scenes. For a query image, histogram intersection on a bag-of-visual-words representation is used to find the set of nearest neighbors in the database. The SIFT flow algorithm then consists of matching densely sampled SIFT features between the two images, while preserving spatial discontinuities. The use of SIFT features allows robust matching across different scene/object appearances and the discontinuity-preserving spatial model allows matching of objects located at different parts of the scene. Experiments show that the proposed approach is able to robustly align complicated scenes with large spatial distortions. We collect a large database of videos and apply the SIFT flow algorithm to two applications: (i) motion field prediction from a single static image and (ii) motion synthesis via transfer of moving objects.


Query Image Alignment Algorithm Motion Prediction Sift Feature Sift Descriptor 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ce Liu
    • 1
  • Jenny Yuen
    • 1
  • Antonio Torralba
    • 1
  • Josef Sivic
    • 2
  • William T. Freeman
    • 1
    • 3
  1. 1.Massachusetts Institute of TechnologyUSA
  2. 2.INRIA/Ecole Normale SupérieureFrance
  3. 3.Adobe SystemsUSA

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