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Proceedings Paper

Fast indexing method for multidimensional nearest-neighbor search
Author(s): John A. Shepherd; Xiaoming Zhu; Nimrod Megiddo
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Paper Abstract

This paper describes a snapshot of work in progress on the development of an efficient file-access method for similarity searching in high-dimensional vector spaces. This method has applications in image databases, where images are accessed via high-dimensional feature vectors, as well as other areas. The technique is based on using a collection of space-filling curves, as an auxiliary indexing structure. Initial performance analyses suggest that the method works as efficiently in moderately high-dimensional spaces (256 dimensions), with tolerable storage and execution-time overhead.

Paper Details

Date Published: 17 December 1998
PDF: 6 pages
Proc. SPIE 3656, Storage and Retrieval for Image and Video Databases VII, (17 December 1998); doi: 10.1117/12.333854
Show Author Affiliations
John A. Shepherd, IBM Almaden Research Ctr. and Univ. of New South Wales (Australia)
Xiaoming Zhu, IBM Almaden Research Ctr. (United States)
Nimrod Megiddo, IBM Almaden Research Ctr. (United States)

Published in SPIE Proceedings Vol. 3656:
Storage and Retrieval for Image and Video Databases VII
Minerva M. Yeung; Boon-Lock Yeo; Charles A. Bouman, Editor(s)

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