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

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.