A new framework for constructing accurate affine invariant regions

Li Tian*, Sei Ichiro Kamata

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


In this study, we propose a simple, yet general and powerful framework for constructing accurate affine invariant regions. In our framework, a method for extracting reliable seed points is first proposed. Then, regions which are invariant to most common affine transformations can be extracted from seed points by two new methods the Path Growing (PG) or the Thresholding Seeded Growing Region (TSGR). After that, an improved ellipse fitting method based on the Direct Least Square Fitting (DLSF) is used to fit the irregularly-shaped contours from the PG or the TSGR to obtain ellipse regions as the final invariant regions. In the experiments, our framework is first evaluated by the criterions of Mikolajczyk's evaluation framework [1], and then by near-duplicate detection problem [2]. Our framework shows its superiorities to the other detectors for different transformed images under Mikolajczyk's evaluation framework and the one with TSGR also gives satisfying results in the application to near-duplicate detection problem.

Original languageEnglish
Pages (from-to)1831-1840
Number of pages10
JournalIEICE Transactions on Information and Systems
Issue number11
Publication statusPublished - 2007 Nov


  • Affine invariant region
  • Ellipse fitting
  • Near-duplicate detection
  • Path Growing
  • Thresholding seeded growing regions

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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