NV-Tree: nearest neighbors at the billion scale

Herwig Lejsek (1) , Björn Þór Jónsson (2) , Laurent Amsaleg (3)

1 Videntifier Technologies
2 School of Computer Science [Reykjavik]
3 TEXMEX - Multimedia content-based indexing

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.

Domains: Databases [cs.DB]; Multimedia [cs.MM]

Herwig Lejsek, Björn Þór Jónsson, Laurent Amsaleg. NV-Tree: nearest neighbors at the billion scale. 1st ACM International Conference on Multimedia Retrieval, Apr 2011, Trento, Italy. ⟨10.1145/1991996.1992050⟩. ⟨hal-00644939⟩