ACM Computing Surveys (CSUR)
A Survey on Content-Based Retrieval for Multimedia Databases
IEEE Transactions on Knowledge and Data Engineering
Towards effective indexing for very large video sequence database
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
iDistance: An adaptive B+-tree based indexing method for nearest neighbor search
ACM Transactions on Database Systems (TODS)
Bayes classification based on minimum bounding spheres
Neurocomputing
An adaptive and dynamic dimensionality reduction method for high-dimensional indexing
The VLDB Journal — The International Journal on Very Large Data Bases
New upper bounds on the quality of the PCA bounding boxes in r2 and r3
SCG '07 Proceedings of the twenty-third annual symposium on Computational geometry
Large-scale similarity-based join processing in multimedia databases
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Indexing methods for efficient protein 3D surface search
Proceedings of the ACM sixth international workshop on Data and text mining in biomedical informatics
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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.