A fast indexing method for multidimensional nearest-neighbor search
John Shepherd,
Xiaoming Zhu,
Nimrod Megiddo
SPIE Conference on Storage and Retrieval for Image and Video Databases VI,
San Jose, California, January 1999
(Compressed Postscript ... 58KB)
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, for example, image databases
where images are accessed via high-dimensional feature vectors.
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
efficiently in moderately high-dimensional spaces (256 dimensions),
with tolerable storage and execution-time overhead.
(The work reported here was performed while John Shepherd was on
study leave at the IBM Almaden Research Center.)
Keys:
content-based image retrieval,
k-nearest-neighbours search,
high-dimensional indexing,
space-filling curves
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