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