High-dimensional indexing with oriented cluster representation for multimedia databases

Rapidly growing multimedia databases have made efficient content-based search an indispensable operation to fast retrieve similar multimedia objects of users' interests. Typically, multimedia objects are represented by high-dimensional feature vectors in the databases. High-dimensional indexing is a primary approach to achieve quick retrieval. In this paper, we present an optimal one-dimensional indexing method which can maximally preserve the original inter-distance between two high-dimensional feature vectors. Such a transformation enables B-tree to achieve its optimal performance. We study a new cluster bounding model called Oriented Minimum Bounding Rectangle (OMBR) which aligns the directions of the rectangle with respect to the orientations of the cluster to achieve a much tighter cluster bound than Minimum Bounding Sphere (MBS) and Minimum Bounding Rectangle (MBR). Obviously, the tighter the cluster bound, the lower the probability for the cluster to intersect with the query search range. Our experiments on real multimedia databases prove the effectiveness of our proposals