Euclidean lattices for high dimensional indexing and searching

Loïc Paulevé 1
1 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : For similarity based searching, multimedia data are represented by one or more numerical vectors: we search the nearest neighbors of the query. Because of the huge number of these data and their high dimension, classical indexing technics are inefficient. The goal of this internship is to study the use of euclidean lattices for database indexing. Lattices have nice properties: they are spatial quantizers, thereby generate a partition of the space and decoding (quantization step) may be done very quickly. Then, we hope to be able to rapidly find a small space region containing data similar to a given query point, without reading all the database.
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Rapport
[Research Report] PI 1903, 2008, pp.57
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https://hal.inria.fr/inria-00326262
Contributeur : Anne Jaigu <>
Soumis le : jeudi 2 octobre 2008 - 13:14:48
Dernière modification le : vendredi 13 janvier 2017 - 14:21:09
Document(s) archivé(s) le : vendredi 4 juin 2010 - 12:06:23

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Loïc Paulevé. Euclidean lattices for high dimensional indexing and searching. [Research Report] PI 1903, 2008, pp.57. <inria-00326262>

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