Exploiting accelerators for efficient high dimensional similarity search
نویسندگان
چکیده
منابع مشابه
Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search
Similarity indices for high-dimensional data are very desirable for building content-based search systems for featurerich data such as audio, images, videos, and other sensor data. Recently, locality sensitive hashing (LSH) and its variations have been proposed as indexing techniques for approximate similarity search. A significant drawback of these approaches is the requirement for a large num...
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Similarity search in high-dimensional vector spaces has been the subject of substantial research, motivated in part by the need to provide query support for images and other complex data types. The paper VLDB 1998 paper "Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces" analyses why this search problem can be so tricky, and shows with intuitiv...
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Most of all index structures based on the R-tree have failed to support ecient indexing mechanisms for similarity search in high-dimensional data spaces. This is due to the fact that most of the index structures commonly use balanced split strategy in order to guarantee storage utilization and the shape of queries for similarity search is a hypersphere in high-dimensional spaces. In this paper...
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Locality Sensitive Hashing (LSH) is one of the most promising techniques for solving nearest neighbour search problem in high dimensional space. Euclidean LSH is the most popular variation of LSH that has been successfully applied in many multimedia applications. However, the Euclidean LSH presents limitations that affect structure and query performances. The main limitation of the Euclidean LS...
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ژورنال
عنوان ژورنال: ACM SIGPLAN Notices
سال: 2016
ISSN: 0362-1340,1558-1160
DOI: 10.1145/3016078.2851144