Fast Similarity Search for High-Dimensional Dataset

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Fast Similarity Search for High-Dimensional Dataset
This paper addresses the challenging problem of rapidly searching and matching high-dimensional features for the applications of multimedia database retrieval and pattern recognition. Most current methods suffer from the problem of dimensionality curse. A number of theoretical and experimental studies lead us to pursue a new approach, called Fast Filtering Vector Approximation (FFVA) to tackle the problem. FFVA is a nearest neighbor search technique that facilitates rapidly indexing and recovering the most similar matches to a highdimensional database of features or spatial data. Extensive experiments have demonstrated effectiveness of the proposed approach.
Quan Wang, Suya You
Added 12 Jun 2010
Updated 12 Jun 2010
Type Conference
Year 2006
Where ISM
Authors Quan Wang, Suya You
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