Correspondence propagation with weak priors

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

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)140-150
Number of pages11
JournalIEEE Transactions on Image Processing
Volume18
Issue number1
DOIs
StatePublished - Jan 5 2009

Keywords

  • Feature matching
  • Image registration
  • Object correspondence
  • Propagation
  • Weak prior

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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    Wang, H., Yan, S., Liu, J., Tang, X., & Huang, T. S. (2009). Correspondence propagation with weak priors. IEEE Transactions on Image Processing, 18(1), 140-150. https://doi.org/10.1109/TIP.2008.2006602