Near-duplicate detection using a new framework of constructing accurate affine invariant regions

  • Authors:

  • Li Tian;Sei-Ichiro Kamata

  • Affiliations:

  • Graduate School of Info., Pro. & Sys., Waseda University, Kitakyushu, Japan;Graduate School of Info., Pro. & Sys., Waseda University, Kitakyushu, Japan

  • Venue:
  • VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
  • Year:
  • 2007

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Abstract

in this study, we propose a simple, yet general and powerful framework for constructing accurate affine invariant regions and use it for near-duplicate detection problem. In our framework, a method for extracting reliable seed points is first proposed. Then, regions which are invariant to most common affine transformations are extracted from seed points by a new method named 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 of TSGRs to obtain ellipse regions as the final invariant regions. At last, SIFT-PCA descriptors are computed on the obtained regions. In the experiment, our framework is evaluated by retrieving near-duplicate in an image database containing 1000 images. It gives a satisfying result of 96.8% precision at 100% recall.