Shape indexing using approximate nearest-neighbour search in high-dimensional spaces
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, f...
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| Published in: | Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition pp. 1000 - 1006 |
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| Main Authors: | , |
| Format: | Conference Proceeding |
| Language: | English |
| Published: |
IEEE
1997
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| Subjects: | |
| ISBN: | 9780818678226, 0818678224 |
| ISSN: | 1063-6919, 1063-6919 |
| Online Access: | Get full text |
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| Summary: | 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. |
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| ISBN: | 9780818678226 0818678224 |
| ISSN: | 1063-6919 1063-6919 |
| DOI: | 10.1109/CVPR.1997.609451 |