Fast Similarity Search for High-Dimensional Dataset

Authors:
Quan Wang;Suya You
Affiliations:
University of Southern California, USA;University of Southern California, USA
Venue:
ISM '06 Proceedings of the Eighth IEEE International Symposium on Multimedia
Year:
2006

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Abstract

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 high-dimensional database of features or spatial data. Extensive experiments have demonstrated effectiveness of the proposed approach.