NDLI: Approximate searches: k-neighbors + precision

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Approximate searches: k-neighbors + precision

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Approximate searches: k-neighbors + precision

Content Provider	ACM Digital Library
Author	Berrani, Sid-Ahmed Amsaleg, Laurent Gros, Patrick
Abstract	It is known that all multi-dimensional index structures fail to accelerate content-based similarity searches when the feature vectors describing images are high-dimensional. It is possible to circumvent this problem by relying on approximate search-schemes trading-off result quality for reduced query execution time. Most approximate schemes, however, provide none or only complex control on the precision of the searches, especially when retrieving the k nearest neighbors (NNs) of query points.In contrast, this paper describes an approximate search scheme for high-dimensional databases where the precision of the search can be probabilistically controlled when retrieving the k NNs of query points. It allows a fine and intuitive control over this precision by setting at run time the maximum probability for a vector that would be in the exact answer set to be missed in the approximate set of answers eventually returned. This paper also presents a performance study of the implementation using real datasets showing its reliability and efficiency. It shows, for example, that our method is 6.72 times faster than the sequential scan when it handles more than 5 $10_{6}$ 24-dimensional vectors, even when the probability of missing one of the true nearest neighbors is below 0.01.
Starting Page	24
Ending Page	31
Page Count	8
File Format	PDF
ISBN	1581137230
DOI	10.1145/956863.956870
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2003-11-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Approximate nearest-neighbor searches Similarity searches Multimedia databases
Content Type	Text
Resource Type	Article

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