@conference {180, title = {HashFile : An Efficient Index
Structure For Multimedia Data}, booktitle = {ICDE}, year =
{2011}, abstract = {Nearest neighbor (NN) search in the high
dimensional vector space is a fundamental but essential query in
many multimedia retrieval applications. Due to the curse of
dimensionality, it is difficult to build indexing support to
efficiently execute NN queries. As the number of feature
dimensions increases, the performance of existing index
structures degrades rapidly and even becomes worse than a simple
sequential scan of data to answer queries. Therefore, an
alternative solution attempts to answer the approximate nearest
neighbor query by adopting the random projection, typically the
locality sensitive hashing (LSH). The hash function can preserve
the distance in the Euclidean space so that similar objects have
a high probability of colliding in the same bucket. The
efficiency is attained by marginally sacrificing the quality of
result sets. Recently, locality sensitive B-tree