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)
Comparing images using joint histograms
Multimedia Systems - Special issue on video content based retrieval
On the geometry of similarity search: dimensionality curse and concentration of measure
Information Processing Letters
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
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
Using the Distance Distribution for Approximate Similarity Queries in High-Dimensional Metric Spaces
DEXA '99 Proceedings of the 10th International Workshop on Database & Expert Systems Applications
Color Co-occurence Descriptors for Querying-by-Example
MMM '98 Proceedings of the 1998 Conference on MultiMedia Modeling
Live@web.com: using CBIR technology in interactive web-TV
Proceedings of the sixth Eurographics workshop on Multimedia 2001
A distributed content-based search engine based on mobile code
Proceedings of the 2005 ACM symposium on Applied computing
Unified framework for fast exact and approximate search in dissimilarity spaces
ACM Transactions on Database Systems (TODS)
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A novel indexing scheme for solving the problem of nearest neighbor queries in generic metric feature spaces for content-based image retrieval is proposed to break the "dimensionality curse." The basis for the proposed method is the partitioning of the feature dataset into 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, effectively cutting down the amount of data to be processed enormously. Results concerning the retrieval accuracy from extensive experimentation with a real image archive are given. The influence of control parameters is investigated with respect to the tradeoff between retrieval accuracy and computational cost.