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Shape Indexing Using Approximate Nearest-Neighbour Search in High-Dimensional Spaces (1997)
| Content Provider | CiteSeerX |
|---|---|
| Author | Lowe, David G. Beis, Jeffrey S. |
| 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 jus... |
| File Format | |
| Journal | Proc. IEEE Conf. Comp. Vision Patt. Recog |
| Publisher Date | 1997-01-01 |
| Access Restriction | Open |
| Subject Keyword | Feature Space Increase Low-dimensional Situation Model Database Query Point High-dimensional Space Rapid Association Approximate Nearest-neighbour Search K-d Tree Search Algorithm Approximate Algorithm Best Bin First Improved Level Large Fraction Complex Object Hash Table Object Model Higherdimensional Space Cluttered Scene New Variant Shape Indexing Close Neighbour Hypothesis Recovery Recognition System |
| Content Type | Text |
