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.

• Type: Conference
• Year: 2011
• Where: ICASSP
• Authors: Hervé Jégou and Romain Tavenard and Matthijs Douze and Laurent Amsaleg
• Keywords: Approximate Search, Large Datasets, Nearest Neighbor Search