Correspondence propagation with weak priors

  • Authors:

  • Huan Wang;Shuicheng Yan;Jianzhuang Liu;Xiaoou Tang;Thomas S. Huang

  • Affiliations:

  • Department of Computer Science, Yale University, New Haven, CT;Department of Electrical and Computer Engineering, National University of Singapore, Singapore;Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong;Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong and Microsoft Research Asia, Beijing, China;Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL

  • Venue:
  • IEEE Transactions on Image Processing
  • Year:
  • 2009

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Abstract

For the problem of image registration, the top few reliable correspondences are often relatively easy to obtain, while the overall matching accuracy may fall drastically as the desired correspondence number increases. In this paper, we present an efficient feature matching algorithm to employ sparse reliable correspondence priors for piloting the feature matching process. First, the feature geometric relationship within individual image is encoded as a spatial graph, and the pairwise feature similarity is expressed as a bipartite similarity graph between two feature sets; then the geometric neighborhood of the pairwise assignment is represented by a categorical product graph, along which the reliable correspondences are propagated; and finally a closed-form solution for feature matching is deduced by ensuring the feature geometric coherency as well as pairwise feature agreements. Furthermore, our algorithm is naturally applicable for incorporating manual correspondence priors for semi-supervised feature matching. Extensive experiments on both toy examples and real-world applications demonstrate the superiority of our algorithm over the state-of-the-art feature matching techniques.