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Similarity search using sparse pivots for efficient multimedia information retrieval (2006)
| Content Provider | CiteSeerX |
|---|---|
| Author | Brisaboa, Nieves R. Fariña, Antonio Pedreira, Oscar Reyes, Nora |
| Description | 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. |
| File Format | |
| Language | English |
| Publisher | IEEE Press |
| Publisher Date | 2006-01-01 |
| Publisher Institution | IN: PROC. OF THE 8TH IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM’06 |
| Access Restriction | Open |
| Subject Keyword | Good Pivot Selection Sparse Pivot Secondary Memory Storage Discrete Distance Function Similarity Search Unstructured Data Source New Pivot-based Method Metric Space Several Vector Experimental Result Fundamental Operation Sparse Spatial Selection Object Insertion Efficient Multimedia Information Retrieval |
| Content Type | Text |
| Resource Type | Article |
