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Keypoint-Based Detection of Near-Duplicate Image Fragments Using Image Geometry and Topology

  • Mariusz Paradowski
  • Andrzej Śluzek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6375)

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.

Keywords

Query Image Planar Object Image Geometry Image Fragment Keypoint Detector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Mariusz Paradowski
    • 1
    • 3
  • Andrzej Śluzek
    • 2
    • 3
  1. 1.Institute of InformaticsWrocław University of TechnologyPoland
  2. 2.Faculty of Physics, Astronomy and InformaticsNicolaus Copernicus UniversityToruńPoland
  3. 3.School of Computer EngineeringNanyang Technological UniversitySingapore

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