Shape indexing using approximate nearest-neighbour search in high-dimensional spaces (1997) [81 citations — 9 self]
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@INPROCEEDINGS{Beis97shapeindexing,author = {Jeffrey S. Beis and David G. Lowe},
title = {Shape indexing using approximate nearest-neighbour search in high-dimensional spaces},
booktitle = {In Proc. IEEE Conf. Comp. Vision Patt. Recog},
year = {1997},
pages = {1000--1006}
}
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Abstract:
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-dimensionalfeatures 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 higherdimensional 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. 1.
