An average-case analysis of MAT and inverted file
Theoretical Computer Science
Common principal components & related multivariate models
Common principal components & related multivariate models
The design and analysis of spatial data structures
The design and analysis of spatial data structures
Index-based object recognition in pictorial data management
Computer Vision, Graphics, and Image Processing
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
International Journal of Computer Vision
Towards an analysis of range query performance in spatial data structures
PODS '93 Proceedings of the twelfth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
NSF workshop on Visual Information Management Systems
ACM SIGMOD Record
CIKM '93 Proceedings of the second international conference on Information and knowledge management
Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
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
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
On the analysis of indexing schemes
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
The Grid File: An Adaptable, Symmetric Multikey File Structure
ACM Transactions on Database Systems (TODS)
Approximating block accesses in database organizations
Communications of the ACM
Analysis and performance of inverted data base structures
Communications of the ACM
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
A Visual Information Management System for the Interactive Retrieval of Faces
IEEE Transactions on Knowledge and Data Engineering
Query by Visual Example - Content based Image Retrieval
EDBT '92 Proceedings of the 3rd International Conference on Extending Database Technology: Advances in Database Technology
Similarity Indexing with the SS-tree
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
VLDB '98 Proceedings of the 24rd 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
An Information-Retrieval Approach for Image Databases
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
An Extendible Hash for Multi-Precision Similarity Querying of Image Databases
Proceedings of the 27th International Conference on Very Large Data Bases
Compact colour descriptors for colour-based image retrieval
Signal Processing - Special section on content-based image and video retrieval
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It has been shown that filtering is a promising way to support efficient content-based retrievals from image data. However, all existing studies on filtering restrict their attention to two levels. In this paper, we consider filtering structures that have at least three levels. In the first half of the paper, by analyzing the CPU and I/O costs of various structures, we provide analytic evidence on why three-level structures can often outperform corresponding two-level ones. We provide further experimental results showing that the three-level structures are typically the best, and can beat the two-level ones by a wide margin. In the second half of the paper, we study how to find the (near-) optimal three-level structure for a given dataset. We develop an optimizer that can handle this task effectively and efficiently. Experimental results indicate that in tens of seconds of CPU time, the optimizer can find a filtering structure whose total runtime per query exceeds that of the real optimal structure by only 2-3 percent .