Metric-Based Shape Retrieval in Large Databases

This paper examines the problem of database organization and retrieval based on computing metric pairwise distances. A low-dimensional Euclidean approximation of a high-dimensional metric space is not efficient, while search in a high-dimensional Euclidean space suffers from the “curse of dimensionality”. Thus, techniques designed for searching metric spaces must be used. We evaluate several such existing exact metric-based indexing techniques, and show that they require extensive computational effort. This motivates the development of an approximate nearest neighbor search technique where the nearest neighbors are used to approximate the local neighborhood of a point. The resulting NN graph is searched in a best-first fashion producing excellent indexing efficiency.