Skip to Main content Skip to Navigation
Conference papers

Query-Adaptative Locality Sensitive Hashing

Hervé Jégou 1 Laurent Amsaleg 2 Cordelia Schmid 1 Patrick Gros 2
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
2 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : It is well known that high-dimensional nearest-neighbor retrieval is very expensive. Many signal processing methods suffer from this computing cost. Dramatic performance gains can be obtained by using approximate search, such as the popular Locality-Sensitive Hashing. This paper improves LSH by performing an on-line selection of the most appropriate hash functions from a pool of functions. An additional improvement originates from the use of $E_8$ lattices for geometric hashing instead of one-dimensional random projections. A performance study based on state-of-the-art high-dimensional descriptors computed on real images shows that our improvements to LSH greatly reduce the search complexity for a given level of accuracy.
Document type :
Conference papers
Complete list of metadata
Contributor : Hervé Jégou Connect in order to contact the contributor
Submitted on : Tuesday, March 15, 2011 - 2:51:35 PM
Last modification on : Thursday, January 20, 2022 - 5:26:51 PM
Long-term archiving on: : Thursday, November 8, 2012 - 11:45:18 AM


Files produced by the author(s)



Hervé Jégou, Laurent Amsaleg, Cordelia Schmid, Patrick Gros. Query-Adaptative Locality Sensitive Hashing. ICASSP 2008 - IEEE International Conference on Acoustics, Speech, and Signal Processing, Mar 2008, Las Vegas, United States. pp.825-828, ⟨10.1109/ICASSP.2008.4517737⟩. ⟨inria-00318614⟩



Les métriques sont temporairement indisponibles