Match Graph Construction for Large Image Databases

  • Kwang In Kim
  • James Tompkin
  • Martin Theobald
  • Jan Kautz
  • Christian Theobalt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7572)


How best to efficiently establish correspondence among a large set of images or video frames is an interesting unanswered question. For large databases, the high computational cost of performing pair-wise image matching is a major problem. However, for many applications, images are inherently sparsely connected, and so current techniques try to correctly estimate small potentially matching subsets of databases upon which to perform expensive pair-wise matching. Our contribution is to pose the identification of potential matches as a link prediction problem in an image correspondence graph, and to propose an effective algorithm to solve this problem. Our algorithm facilitates incremental image matching: initially, the match graph is very sparse, but it becomes dense as we alternate between link prediction and verification. We demonstrate the effectiveness of our algorithm by comparing it with several existing alternatives on large-scale databases. Our resulting match graph is useful for many different applications. As an example, we show the benefits of our graph construction method to a label propagation application which propagates user-provided sparse object labels to other instances of that object in large image collections.


Image matching graph construction link prediction 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kwang In Kim
    • 1
  • James Tompkin
    • 1
    • 2
    • 3
  • Martin Theobald
    • 1
  • Jan Kautz
    • 2
  • Christian Theobalt
    • 1
  1. 1.Max-Planck-Institut für InformatikSaarbrückenGermany
  2. 2.University College LondonLondonUK
  3. 3.Intel Visual Computing InstituteSaarbrückenGermany

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