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2006
IEEE

Similarity Search Using Sparse Pivots for Efficient Multimedia Information Retrieval

11 years 9 months ago
Similarity Search Using Sparse Pivots for Efficient Multimedia Information Retrieval
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.
Nieves R. Brisaboa, Antonio Fariña, Oscar P
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2006
Where ISM
Authors Nieves R. Brisaboa, Antonio Fariña, Oscar Pedreira, Nora Reyes
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