The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
On the geometry of similarity search: dimensionality curse and concentration of measure
Information Processing Letters
Pattern Recognition and Image Processing
Pattern Recognition and Image Processing
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
A Survey on Content-Based Retrieval for Multimedia Databases
IEEE Transactions on Knowledge and Data Engineering
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Parallel k/h-Means Clustering for Large Data Sets
Euro-Par '99 Proceedings of the 5th International Euro-Par Conference on Parallel Processing
Efficient Image Retrieval through Vantage Objects
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
Approximate similarity search: A multi-faceted problem
Journal of Discrete Algorithms
A new continuous nearest neighbor technique for query processing on mobile environments
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part II
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An indexing scheme for solving the problem of nearest neighbor queries in generic metric feature spaces for content-based retrieval is proposed aiming to break the "dimensionality curse". The basis for the proposed method is the partitioning of the feature dataset into a fixed number of clusters that are represented by single buoys. Upon submission of a query request, only a small number of clusters whose buoys are close to the query object are considered for the approximate query result, cutting down the amount of data to be processed effectively. Results from extensive experimentation concerning the retrieval accuracy are given. The influence of control parameters is investigated with respect to the tradeoff between retrieval accuracy and query execution time.