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ICASSP
2011
IEEE

Searching in one billion vectors: re-rank with source coding

9 years 3 months ago
Searching in one billion vectors: re-rank with source coding
Recent indexing techniques inspired by source coding have been shown successful to index billions of high-dimensional vectors in memory. In this paper, we propose an approach that re-ranks the neighbor hypotheses obtained by these compressed-domain indexing methods. In contrast to the usual post-verification scheme, which performs exact distance calculation on the short-list of hypotheses, the estimated distances are refined based on short quantization codes, to avoid reading the full vectors from disk. We have released a new public dataset of one billion 128-dimensional vectors and proposed an experimental setup to evaluate high dimensional indexing algorithms on a realistic scale. Experiments show that our method accurately and efficiently re-ranks the neighbor hypotheses using little memory compared to the full vectors representation.
Hervé Jégou and Romain Tavenard and Matthijs Dou
Added 15 Mar 2011
Updated 15 Mar 2011
Type Conference
Year 2011
Where ICASSP
Authors Hervé Jégou and Romain Tavenard and Matthijs Douze and Laurent Amsaleg
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