An algorithm for finding nearest neighbours in (approximately) constant average time
Pattern Recognition Letters
Distance-based indexing for high-dimensional metric spaces
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Some approaches to best-match file searching
Communications of the ACM
ACM Computing Surveys (CSUR)
Fixed Queries Array: A Fast and Economical Data Structure for Proximity Searching
Multimedia Tools and Applications
Near Neighbor Search in Large Metric Spaces
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Proximity Matching Using Fixed-Queries Trees
CPM '94 Proceedings of the 5th Annual Symposium on Combinatorial Pattern Matching
Pivot selection techniques for proximity searching in metric spaces
Pattern Recognition Letters
A compact space decomposition for effective metric indexing
Pattern Recognition Letters
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Selecting vantage objects for similarity indexing
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Similarity Search Using Sparse Pivots for Efficient Multimedia Information Retrieval
ISM '06 Proceedings of the Eighth IEEE International Symposium on Multimedia
A Dynamic Pivot Selection Technique for Similarity Search
SISAP '08 Proceedings of the First International Workshop on Similarity Search and Applications (sisap 2008)
Effective Use of Space for Pivot-Based Metric Indexing Structures
SISAP '08 Proceedings of the First International Workshop on Similarity Search and Applications (sisap 2008)
Reference-based indexing for metric spaces with costly distance measures
The VLDB Journal — The International Journal on Very Large Data Bases
Simple space-time trade-offs for AESA
WEA'07 Proceedings of the 6th international conference on Experimental algorithms
A fast pivot-based indexing algorithm for metric spaces
Pattern Recognition Letters
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We consider the problem of similarity search in metric spaces with costly distance functions and large databases. There is a trade-off between the amount of information stored in the index and the reduction in the number of comparisons for solving a query. Pivot-based methods clearly outperform clustering-based ones in number of comparisons, but their space requirements are higher and this can prevent their application in real problems. Therefore, several strategies have been proposed that reduce the space needed by pivot-based methods, as BAESA, FQA or KVP. In this paper, we analyze the usefulness of pivots depending on their proximity to the object. As consequence of this analysis, we propose a new pivot-based method that requires an amount of space equal or very close to that needed by clustering-based methods. We provide experimental results that show that our proposal represents a competitive strategy to clustering oriented solutions when using the same amount of memory.