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Image search can be viewed as a problem of large-scale approximate nearest neighbor (ANN) search in image feature space. Hash ranking methods have been widely used for ANN search because of their two benefits: less memory usage and high search efficiency. Generally, the hash ranking methods face two problems: binary encoding and binary code ranking. This paper focuses on the latter. In existing work,...
With the advantage of computational and storage efficiency, binary encoding and Hamming distance are widely used in approximate nearest neighbor search. However, a large number of candidate points share the same Hamming distance with the query when the database contains too many points, leading to the issue of confusing ranking. Therefore, asymmetric ranking is proposed to address this issue, in which...
Binary hashing based methods have been widely used for large-scale approximate nearest neighbor search because of their two benefits: less memory usage and high search efficiency. In these methods, binary code ranking is usually implemented based on Hamming distance or asymmetric distance. Generally, asymmetric distance is more accurate than Hamming distance, thus recent work focuses on the asymmetric...
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