Searching Near-Replicas of Images via Clustering

	Edward Chang, Chen Li, James Wang, Peter Mork and Gio Wiederhold
	Department of Computer Science, Stanford University 
	echang,chenli,wangz,pmork,giog@cs.stanford.edu

			Abstract

 Internet piracy has been one of the major concerns for Web publishing. In
  study we present a system, RIME, that we have prototyped for detecting
  unauthorized image copying on the World-Wide Web. To speed up the copy
  detection, RIME uses a new clustering/hashing approach that first
  clusters similar images on adjacent disk cylinders and then builds
  indexes to access the clusters made in this way. Searching for the
  replicas of an image often takes just one IO to look up the location of
  the cluster containing similar objects and one sequential file IO to
  read in this cluster. Our experimental results show that RIME can detect
  image copies both more efficiently and effectively than the traditional
  content-based image retrieval systems that use tree-like structures to
  index images. In addition, RIME copes well with image format conversion,
  resampling, requantization and geometric transformations. 

  Keywords: clustering, copy detection, multidimensional indexes,
	    similarity search.