Anti-sparse coding for approximate nearest neighbor search

Hervé Jégou 1 Teddy Furon 1 Jean-Jacques Fuchs 2
1 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
2 TEMICS - Digital image processing, modeling and communication
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : This paper proposes a binarization scheme for vectors of high dimension based on the recent concept of anti-sparse coding, and shows its excellent performance for approximate nearest neighbor search. Unlike other binarization schemes, this framework allows, up to a scaling factor, the explicit reconstruction from the binary representation of the original vector. The paper also shows that random projections which are used in Locality Sensitive Hashing algorithms, are significantly outperformed by regular frames for both synthetic and real data if the number of bits exceeds the vector dimensionality, i.e., when high precision is required.
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https://hal.inria.fr/inria-00633193
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Submitted on : Monday, October 24, 2011 - 6:48:08 PM
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  • HAL Id : inria-00633193, version 2
  • ARXIV : 1110.3767

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Hervé Jégou, Teddy Furon, Jean-Jacques Fuchs. Anti-sparse coding for approximate nearest neighbor search. [Research Report] RR-7771, INRIA. 2011. ⟨inria-00633193v2⟩

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