an efficient parts-based near-duplicate and sub-image retrieval system
We introduce a system for near-duplicate detection and sub-image retrieval. Such a system is useful for finding copyright violations and detecting forged images. We define near-duplicate as images altered with common transformations such as changing contrast, saturation, scaling, cropping, framing, etc. Our system builds a parts-based representation of images using  distinctive local descriptors  which give high quality matches even under severe transformations. To cope with the large number of features extracted from the images, we employ  locality-sensitive hashing  to index the local descriptors. This allows us to make approximate similarity queries that only examine a small fraction of the database. Although locality-sensitive hashing has excellent theoretical performance properties, a standard implementation would still be unacceptably slow for this application. We show that, by optimizing layout and access to the index data on disk, we can efficiently query indices containing millions of keypoints. Our system achieves near-perfect accuracy (100\% precision at 99.85\% recall) on the tests presented in Meng  et al.  [16], and consistently strong results on our own, significantly more challenging experiments. Query times are interactive even for collections of thousands of images.
efficiency sub-image retrieval imaging systems copyright scale representation locality quality feature hashing indexing approximation similarity queries queries data database performance standardization implementation applications optimality layout access accuracy precise experience interaction collect sensitive near-duplicate image detection locality-sensitive hashing (lsh) local image descriptors interest points local sensitive hash duplicate detection image retrieval query