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Shape indexing using approximate nearest-neighbour search in high-dimensional spaces

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2 Author(s)
J. S. Beis ; Dept. of Comput. Sci., British Columbia Univ., Vancouver, BC, Canada ; D. G. Lowe

Shape indexing is a way of making rapid associations between features detected in an image and object models that could have produced them. When model databases are large, the use of high-dimensional features is critical, due to the improved level of discrimination they can provide. Unfortunately, finding the nearest neighbour to a query point rapidly becomes inefficient as the dimensionality of the feature space increases. Past indexing methods have used hash tables for hypothesis recovery, but only in low-dimensional situations. In this paper we show that a new variant of the k-d tree search algorithm makes indexing in higher-dimensional spaces practical. This Best Bin First, or BBF search is an approximate algorithm which finds the nearest neighbour for a large fraction of the queries, and a very close neighbour in the remaining cases. The technique has been integrated into a fully developed recognition system, which is able to detect complex objects in real, cluttered scenes in just a few seconds

Published in:

Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on

Date of Conference:

17-19 Jun 1997