Image registration using BP-SIFT

Y Zhu, Samuel Cheng, Vladimir Stankovic, Lina Stankovic

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)


Scale Invariant Feature Transform (SIFT) is a powerful technique for image registration. Although SIFT descriptors accurately extract invariant image characteristics around keypoints, the commonly used matching approaches of registration loosely represent the geometric information among descriptors. In this paper, we propose an image registration algorithm named BP-SIFT, where we formulate keypoint matching of SIFT descriptors as a global optimization problem and provide a suboptimum solution using belief propagation (BP). Experimental results show significant improvement over conventional SIFT-based matching with reasonable computation complexity.
Original languageEnglish
Pages (from-to)448-457
Number of pages10
JournalJournal of Visual Communication and Image Representation
Issue number4
Publication statusPublished - 1 May 2013


  • image registration
  • belief propagation
  • min-sum algorithm
  • keypoint matching
  • descriptors matching
  • image processing


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