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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... (Update)
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BibTeX entry: (Update)
N. Katayama and S. Satoh, "The sr-tree: An index structure for high-dimensional nearest neighbor queries," Proceedings of ACM SIGMOD , May 1997. http://citeseer.ist.psu.edu/katayama97srtree.html More
@inproceedings{ katayama97srtree,
author = "Norio Katayama and Shin'ichi Satoh",
title = "The {SR-tree}: an index structure for high-dimensional nearest neighbor queries",
pages = "369--380",
year = "1997",
url = "citeseer.ist.psu.edu/katayama97srtree.html" }
Citations (may not include all citations):643 Query by Image and Video Content: the QBIC System (context) - Flickner, Sawhney et al. - 1995
162 Similarity Indexing with the SS-tree (context) - White, Jain - 1996
76 Intelligent Access to Digital Video: Informedia Project (context) - Wactlar, Kanade et al. - 1996
1 tree: an Efficient and Robust Access (context) - Beckmann, Kriegel et al.
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