Data-oriented locality sensitive hashing

Locality Sensitive Hashing (LSH) has been proposed as a scalable and high-dimensional index for approximate similarity search. Euclidean LSH is a variation of LSH and has been successfully used in many multimedia applications. However, hash functions of the basic Euclidean LSH project data points over randomly selected directions, which reduces accuracy when data are non-uniformly distributed. So more hash tables are needed to guarantee the accuracy, and thus more memory is consumed. Since heavy memory cost is a significant drawback of Euclidean LSH, we propose Data-Oriented LSH to reduce memory consumption when data are non-uniformly distributed. Most of existing methods are query-directed, such as multi-probe and query expansion methods. We focused on the hash table construction, and thus the query-directed methods can be applied to our index to improve further. The experiment shows that to achieve the same accuracy, our method uses less time and less memory compared with original Euclidean LSH.