The design and analysis of spatial data structures
The design and analysis of spatial data structures
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
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
Point location in arrangements of hyperplanes
Information and Computation
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Photobook: content-based manipulation of image databases
International Journal of Computer Vision
A model for the prediction of R-tree performance
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Distance-based indexing for high-dimensional metric spaces
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
The SR-tree: an index structure for high-dimensional nearest neighbor queries
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
A cost model for nearest neighbor search in high-dimensional data space
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Visual information retrieval from large distributed online repositories
Communications of the ACM
A cost model for similarity queries in metric spaces
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Multidimensional access methods
ACM Computing Surveys (CSUR)
On the geometry of similarity search: dimensionality curse and concentration of measure
Information Processing Letters
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Multidimensional divide-and-conquer
Communications of the ACM
ACM Computing Surveys (CSUR)
ACM Transactions on Graphics (TOG)
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
The TV-tree: an index structure for high-dimensional data
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
Estimating the Intrinsic Dimension of Data with a Fractal-Based Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Feature Selection Applied to Content-Based Retrieval of Lung Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Similarity Indexing with the SS-tree
ICDE '96 Proceedings of the Twelfth International Conference on 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
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Contrast Plots and P-Sphere Trees: Space vs. Time in Nearest Neighbour Searches
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Near Neighbor Search in Large Metric Spaces
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Fast Nearest Neighbor Search in Medical Image Databases
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
IEEE Transactions on Knowledge and Data Engineering
Properties of Embedding Methods for Similarity Searching in Metric Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval Systems
ASSET '99 Proceedings of the 1999 IEEE Symposium on Application - Specific Systems and Software Engineering and Technology
Deflating the Dimensionality Curse Using Multiple Fractal Dimensions
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Supporting Content-based Queries over Images in MARS
ICMCS '97 Proceedings of the 1997 International Conference on Multimedia Computing and Systems
Divide and conquer algorithms for closest point problems in multidimensional space.
Divide and conquer algorithms for closest point problems in multidimensional space.
Optimal embedding for shape indexing in medical image databases
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Geodesic entropic graphs for dimension and entropy estimation in manifold learning
IEEE Transactions on Signal Processing
Computer Vision and Image Understanding
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Fast similarity retrieval is critical for content-based image retrieval systems. Tree indexing is a classical technique for fast retrieval, but the practical performance increase offered by the indexing tree depends on the intrinsic dimension of the data. Data with a low intrinsic dimension can be indexed more efficiently than data with high intrinsic dimension. This suggests that an indexing tree that is adapted to the data distribution may be more efficient. This paper proposes two adaptation procedures that are guaranteed to improve indexing efficiency. The procedures are based on a formula for average number of node tests incurred during the retrieval. The formula clearly shows how indexing performance varies with the distribution of feature points and the query. Greedy and optimal tree adaptation procedures are derived based on the formula. Both procedures explicitly enhance the retrieval performance of indexing trees. The optimally adapted tree carries the mathematical guarantee that it is the best performing tree in a set of possible trees obtained by node elimination. The adaptation procedures are applied to kdb-trees and hierarchical clustering trees for indexing synthetic as well as real data sets in medical image databases. Experimental results validate the claim that adaptation procedures increase retrieval efficiency.