The SR-tree: An Index Structure for High-Dimensional Nearest Neighbor Queries.

Norio Katayama, Shin'ichi Satoh: The SR-tree: An Index Structure for High-Dimensional Nearest Neighbor Queries. SIGMOD Conference 1997: 369-380

@inproceedings{DBLP:conf/sigmod/KatayamaS97,
  author    = {Norio Katayama and
               Shin'ichi Satoh},
  editor    = {Joan Peckham},
  title     = {The SR-tree: An Index Structure for High-Dimensional Nearest
               Neighbor Queries},
  booktitle = {SIGMOD 1997, Proceedings ACM SIGMOD International Conference
               on Management of Data, May 13-15, 1997, Tucson, Arizona, USA},
  publisher = {ACM Press},
  year      = {1997},
  pages     = {369-380},
  ee        = {http://doi.acm.org/10.1145/253260.253347, db/conf/sigmod/KatayamaS97.html},
  crossref  = {DBLP:conf/sigmod/97},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

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 highdimensional and non-uniform data which can be practical in actual image/video similarity indexing. We include the performance test results that 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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Joan Peckham (Ed.): SIGMOD 1997, Proceedings ACM SIGMOD International Conference on Management of Data, May 13-15, 1997, Tucson, Arizona, USA. ACM Press 1997

, SIGMOD Record 26(2), June 1997
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References

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