Feature matching with affine-function transformation models

Hongsheng Li, Xiaolei Huang, Junzhou Huang, Shaoting Zhang

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Feature matching is an important problem and has extensive uses in computer vision. However, existing feature matching methods support either a specific or a small set of transformation models. In this paper, we propose a unified feature matchingframework which supports a large family of transformation models. We call the family of transformation models the affine-functionfamily, in which all transformations can be expressed by affine functions with convex constraints. In this framework, the goal is to recover transformation parameters for every feature point in a template point set to calculate their optimal matching positions in an input image. Given pairwise feature dissimilarity values between all points in the template set and the input image, we create a convexdissimilarity function for each template point. Composition of such convex functions with any transformation model in the affine-function family is shown to have an equivalent convex optimization form that can be optimized efficiently. Four example transformation models in the affine-function family are introduced to show the flexibility of our proposed framework. Our framework achieves 0.0 percent matching errors for both CMU House and Hotel sequences following the experimental setup in [6].

Original languageEnglish (US)
Article number2324568
Pages (from-to)2407-2422
Number of pages16
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number12
StatePublished - Dec 1 2014

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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