Index-driven similarity search in metric spaces (2003)
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| Venue: | ACM Transactions on Database Systems |
| Citations: | 118 - 6 self |
BibTeX
@ARTICLE{Hjaltason03index-drivensimilarity,
author = {Gisli R. Hjaltason and Hanan Samet},
title = {Index-driven similarity search in metric spaces},
journal = {ACM Transactions on Database Systems},
year = {2003},
volume = {28},
pages = {2003}
}
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Abstract
Similarity search is a very important operation in multimedia databases and other database applications involving complex objects, and involves finding objects in a data set S similar to a query object q, based on some similarity measure. In this article, we focus on methods for similarity search that make the general assumption that similarity is represented with a distance metric d. Existing methods for handling similarity search in this setting typically fall into one of two classes. The first directly indexes the objects based on distances (distance-based indexing), while the second is based on mapping to a vector space (mapping-based approach). The main part of this article is dedicated to a survey of distance-based indexing methods, but we also briefly outline how search occurs in mapping-based methods. We also present a general framework for performing search based on distances, and present algorithms for common types of queries that operate on an arbitrary “search hierarchy. ” These algorithms can be applied on each of the methods presented, provided a suitable search hierarchy is defined.







