Efficient similarity search and classification via rank aggregation
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
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ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Theory of nearest neighbors indexability
ACM Transactions on Database Systems (TODS)
A posteriori multi-probe locality sensitive hashing
MM '08 Proceedings of the 16th ACM international conference on Multimedia
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Proceedings of the ACM International Conference on Image and Video Retrieval
Building a web-scale image similarity search system
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Proceedings of the 2nd ACM workshop on Multimedia in forensics, security and intelligence
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Computer Vision and Image Understanding
Indexing and searching 100M images with map-reduce
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
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This paper presents the NV-Tree (Nearest Vector Tree). It addresses the specific, yet important, problem of efficiently and effectively finding the approximate k-nearest neighbors within a collection of a few billion high-dimensional data points. The NV-Tree is a very compact index, as only six bytes are kept in the index for each high-dimensional descriptor. It thus scales extremely well when indexing large collections of high-dimensional descriptors. The NV-Tree efficiently produces results of good quality, even at such a large scale that the indices cannot be kept entirely in main memory any more. We demonstrate this with extensive experiments using a collection of 2.5 billion SIFT (Scale Invariant Feature Transform) descriptors.