The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
The SR-tree: an index structure for high-dimensional nearest neighbor queries
Abstract
Recently, similarity queries on feature vectors have been widely used to perform content-based retrieval of images. To apply this technique to large databases, it is required to develop multidimensional index structures supporting nearest neighbor queries efficiently. The SS-tree had been proposed for this purpose and is known to outperform other index structures such as the R*-tree and the K-D-B-tree. One of its most important features is that it employs bounding spheres rather than bounding rectangles for the shape of regions. However, we demonstrate in this paper that bounding spheres occupy much larger volume than bounding rectangles with high-dimensional data and that this reduces search efficiency. To overcome this drawback, we propose a new index structure called the SR-tree (Sphere/Rectangle-tree) which integrates bounding spheres and bounding rectangles. A region of the SR-tree is specified by the intersection of a bounding sphere and a bounding rectangle. Incorporating bounding rectangles permits neighborhoods to be partitioned into smaller regions than the SS-tree and improves the disjointness among regions. This enhances the performance on nearest neighbor queries especially for high-dimensional and non-uniform data which can be practical in actual image/video similarity indexing. We include the performance test results the verify this advantage of the SR-tree and show that the SR-tree outperforms both the SS-tree and the R*-tree.
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Cites
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Cited by
(1999) · Cited by 3,186
When Is ''Nearest Neighbor'' Meaningful?
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On the Surprising Behavior of Distance Metrics in High Dimensional Space
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A survey on unsupervised outlier detection in high‐dimensional numerical data
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Retrieving articulated 3-D models using medial surfaces
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Metadata
- Authors
- Katayama, N., & Satoh, S.
- Venue
- Proceedings of the 1997 ACM SIGMOD international conference on Management of data - SIGMOD '97
- Publisher
- ACM Press
- Published
- 1997
- Pages
- 369-380
- ISSN
- 0730-8078 (ISSN-L)
- OpenAlex
- W2145725688
- Cited by
- 817
- References
- 14
- FWCI
- 43.06
- Type
- conference paper
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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