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-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 ju...

• Type: Conference
• Year: 1997
• Where: CVPR
• Authors: Jeffrey S. Beis, David G. Lowe
• Keywords: Algorithm Makes Indexing, Close Neighbour, Computer Vision, CVPR 1997, Feature Space Increases, Nearest Neighbour, Shape Indexing