Efficient Similarity Search in Scientific Databases with Feature Signatures

The recent rapid growth of scientific data necessitates efficient similarity search techniques for which convenient object representation models are of vital importance. Feature signatures denoting highly flexible object feature representations have increasingly gained attention for which corresponding efficiency improvement techniques are developed. In this paper, we focus on efficient query processing with the well-known Earth Mover's Distance (EMD) on databases of feature signatures, and propose efficient approximation techniques successfully applicable to high-dimensional feature signatures via dimensionality reduction, guaranteeing both completeness and no false-dismissal within a filter-and-refine architecture. Rigorous experiments on real world data indicate a considerable reduction in the number of EMD computations and high efficiency of the proposed techniques which significantly reduce the query processing time.