  Image copy detection is challenging and appealing topic in computer vision
and signal processing. Recent advancements in multimedia have made distribution
of image across the global easy and fast: that leads to many other issues such
as forgery and image copy retrieval.
  Local keypoint descriptors such as SIFT are used to represent the images, and
based on those descriptors matching, images are matched and retrieved. Features
are quantized so that searching/matching may be made feasible for large
databases at the cost of accuracy loss. In this paper, we propose binary
feature that is obtained by quantizing the SIFT into binary, and rank list is
re-examined to remove the false positives. Experiments on challenging dataset
shows the gain in accuracy and time.
