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

Image and Video Indexing and Retrieval

Full Paper at IEEE Xplore

Přednášející: Hervé Jégou, Autoři: 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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  Informace o přednášce

Nahráno: 2011-05-25 16:15 - 16:35, Club A
Přidáno: 14. 6. 2011 22:17
Počet zhlédnutí: 61
Rozlišení videa: 1024x576 px, 512x288 px
Délka videa: 0:17:52
Audio stopa: MP3 [6.03 MB], 0:17:52