Keypoint-based detection of near-duplicate image fragments using image geometry and topology

  • Authors:

  • Mariusz Paradowski;Andrzej Šluzek

  • Affiliations:

  • Institute of Informatics, Wrocław University of Technology, Poland and School of Computer Engineering, Nanyang Technological University, Singapore;Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, Toruń, Poland and School of Computer Engineering, Nanyang Technological University, Singapore

  • Venue:
  • ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part II
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

One of the advanced techniques in visual information retrieval is detection of near-duplicate fragments, where the objective is to identify images containing almost exact copies of unspecified fragments of a query image. Such near-duplicates would typically indicate the presence of the same object in images. Thus, the assumed differences between near-duplicate fragments should result either from image-capturing settings (illumination, viewpoint, camera parameters) or from the object's deformation (e.g. location changes, elasticity of the object, etc.). The proposed method of near-duplicate fragment detection exploits statistical properties of keypoint similarities between compared images. Two cases are discussed. First, we assume that near-duplicates are (approximately) related by affine transformations, i.e. the underlying objects are locally planar. Secondly, we allow more random distortions so that a wider range of objects (including deformable ones) can be considered. Thus, we exploit either the image geometry or image topology. Performances of both approaches are presented and compared.