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

• Published in: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition pp. 1000 - 1006
• Main Authors: Beis, J.S., Lowe, D.G.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 1997
• Subjects: Computer science; Computer vision; Image databases; Indexing; Layout; Neural networks; Object detection; Shape; Spatial databases; Tree data structures
• ISBN: 9780818678226; 0818678224
• ISSN: 1063-6919; 1063-6919
• DOI: 10.1109/CVPR.1997.609451

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.