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
Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
Searching Multimedia Databases by Content
Searching Multimedia Databases by Content
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Hilbert R-tree: An Improved R-tree using Fractals
VLDB '94 Proceedings of the 20th 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
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
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This chapter presents a new indexing scheme, the Subspace Coding Method (SCM), that offers high performance similarity retrieval. Indices in the R-tree family generate tree structure using MBRs (Minimum bounding rectangles) or MBSs (Minimum bounding spheres). Our proposed method is based on tree structure using MBRs (i.e. the R-tree, the R*-tree, the X-tree and so on), and newly introduced the notion of VBR (Virtual Bounding Rectangle). VBRs are rectangles which contain and approximate MBRs. Importantly, the notion of VBR is orthogonal to any other method in the field of spatial search and is introduced into any spatial indices using MBRs. Furthermore, we have introduced the Subspace Coding Method, which can compactly represent VBR. The performance evaluation shows the superiority of our method for high-dimensional data.