A Dimensionality Reduction Method for Efficient Search of High-Dimensional Databases

SUMMARY In this paper, we present a novel approach for efficient search of high-dimensional databases, such as video shots. The idea is to map feature vectors from the highdimensional feature space into a point in a low-dimensional distance space. Then, a spatial access method, such as an R-tree, is used to cluster these points based on their distances in the low-dimensional space. Our mapping method, called topological mapping, guarantees no false dismissals in the result of a query. However, the result of a query might contain some false alarms. Hence, two refinement steps are performed to remove these false alarms. Comparative experiments on a database of video shots show the superior efficiency of the topological mapping method over other known methods.