SEARCHING IN ONE BILLION VECTORS: RE-RANK WITH SOURCE CODING

Image and Video Indexing and Retrieval

Presented by: Hervé Jégou, Author(s): Hervé Jégou, INRIA, France; Romain Tavenard, University of Rennes 1, France; Matthijs Douze, INRIA, France; Laurent Amsaleg, CNRS, France

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.

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