Bitmap indexing method for complex similarity queries with relevance feedback
MMDB '03 Proceedings of the 1st ACM international workshop on Multimedia databases
The Active Vertice method: a performant filtering approach to high-dimensional indexing
Data & Knowledge Engineering
Filter ranking in high-dimensional space
Data & Knowledge Engineering
Using high dimensional indexes to support relevance feedback based interactive images retrieval
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Hierarchical Indexing Structure for Efficient Similarity Search in Video Retrieval
IEEE Transactions on Knowledge and Data Engineering
Efficient high-dimensional indexing by sorting principal component
Pattern Recognition Letters
Approximate Retrieval with HiPeR: Application to VA-Hierarchies
MMM '09 Proceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling
A flexible framework to ease nearest neighbor search in multidimensional data spaces
Data & Knowledge Engineering
Indexing structures for content-based retrieval of large image databases: a review
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
VA-files vs. r*-trees in distance join queries
ADBIS'05 Proceedings of the 9th East European conference on Advances in Databases and Information Systems
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We propose a new dynamic index structure called the GC-tree (or the grid cell tree) for efficient similarity search in image databases. The GC-tree is based on a special subspace partitioning strategy which is optimized for a clustered high-dimensional image dataset. The basic ideas are threefold: 1) we adaptively partition the data space based on a density function that identifies dense and sparse regions in a data space; 2) we concentrate the partition on the dense regions, and the objects in the sparse regions of a certain partition level are treated as if they lie within a single region; and 3) we dynamically construct an index structure that corresponds to the space partition hierarchy. The resultant index structure adapts well to the strongly clustered distribution of high-dimensional image datasets. To demonstrate the practical effectiveness of the GC-tree, we experimentally compared the GC-tree with the IQ-tree, LPC-file, VA-file, and linear scan. The result of our experiments shows that the GC-tree outperforms all other methods.