Reference-based indexing for metric spaces with costly distance measures
The VLDB Journal — The International Journal on Very Large Data Bases
Optimal Pivots to Minimize the Index Size for Metric Access Methods
SISAP '09 Proceedings of the 2009 Second International Workshop on Similarity Search and Applications
Visual-semantic graphs: using queries to reduce the semantic gap in web image retrieval
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
A fast pivot-based indexing algorithm for metric spaces
Pattern Recognition Letters
Selecting vantage objects for similarity indexing
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
kNN query processing in metric spaces using GPUs
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part I
Sparse spatial selection for novelty-based search result diversification
SPIRE'11 Proceedings of the 18th international conference on String processing and information retrieval
Indexing dense nested metric spaces for efficient similarity search
PSI'09 Proceedings of the 7th international Andrei Ershov Memorial conference on Perspectives of Systems Informatics
Modelling efficient novelty-based search result diversification in metric spaces
Journal of Discrete Algorithms
Parameter-free and domain-independent similarity search with diversity
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Range query processing on single and multi GPU environments
Computers and Electrical Engineering
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Similarity search is a fundamental operation for applications that deal with unstructured data sources. In this paper we propose a new pivot-based method for similarity search, called Sparse Spatial Selection (SSS). This method guarantees a good pivot selection more efficiently than other methods previously proposed. In addition, SSS adapts itself to the dimensionality of the metric space we are working with, and it is not necessary to specify in advance the number of pivots to extract. Furthermore, SSS is dynamic, it supports object insertions in the database efficiently, it can work with both continuous and discrete distance functions, and it is suitable for secondary memory storage. In this work we provide experimental results that confirm the advantages of the method with several vector and metric spaces.