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A posteriori multi-probe locality sensitive hashing (2008)
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| Venue: | ACM Multimedia |
| Citations: | 40 - 11 self |
BibTeX
@ARTICLE{Joly08aposteriori,
author = {Alexis Joly and Olivier Buisson},
title = {A posteriori multi-probe locality sensitive hashing},
journal = {ACM Multimedia},
year = {2008}
}
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Abstract
Efficient high-dimensional similarity search structures are essential for building scalable content-based search systems on feature-rich multimedia data. In the last decade, Locality Sensitive Hashing (LSH) has been proposed as indexing technique for approximate similarity search. Among the most recent variations of LSH, multiprobe LSH techniques have been proved to overcome the overlinear space cost drawback of common LSH. Multi-probe LSH is built on the well-known LSH technique, but it intelligently probes multiple buckets that are likely to contain query results in a hash table. Our method is inspired by previous work on probabilistic similarity search structures and improves upon recent theoretical work on multi-probe and query adaptive LSH. Whereas these methods are based on likelihood criteria that a given bucket contains query results, we define a more reliable a posteriori model taking account some prior about the queries and the searched objects. This prior knowledge allows a better quality control of the search and a more accurate selection of the most probable buckets. We implemented a nearest neighbors search based on this paradigm and performed experiments on different real visual features datasets. We show that our a posteriori scheme outperforms other multi-probe LSH while offering a better quality control. Comparisons to the basic LSH technique show that our method allows consistent improvements both in space and time efficiency.
Keyphrases
posteriori multi-probe locality sensitive hashing query result multi-probe lsh quality control likelihood criterion approximate similarity search common lsh well-known lsh technique last decade recent variation multiple bucket scalable content-based search system different real visual feature datasets feature-rich multimedia data overlinear space cost drawback basic lsh technique show previous work locality sensitive hashing indexing technique time efficiency query adaptive lsh upon recent theoretical work efficient high-dimensional similarity search structure consistent improvement posteriori model prior knowledge probable bucket searched object accurate selection probabilistic similarity search structure posteriori scheme hash table multiprobe lsh technique
