Content Adaptive Hash Lookups for Near-Duplicate Image Search by Full or Partial Image Queries

In this paper we present a scalable and high performance near-duplicate image search method. The proposed algorithm follows the common paradigm of computing local features around repeatable scale invariant interest points. Unlike existing methods, much shorter hashes are used (40 bits). By leveraging on the shortness of the hashes, a novel high performance search algorithm is introduced which analyzes the reliability of each bit of a hash and performs content adaptive hash lookups by adaptively adjusting the "range" of each hash bit based on reliability. Matched features are post-processed to determine the final match results. We experimentally show that the algorithm can detect cropped, resized, print-scanned and re-encoded images and pieces from images among thousands of images. The proposed algorithm can search for a 200x200 piece of image in a database of 2,250 images with size 2400x4000 in 0.020 seconds on 2.5GHz Intel Core 2.