HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Journal articles

Locality sensitive hashing: a comparison of hash function types and querying mechanisms

Loïc Paulevé 1 Hervé Jégou 2 Laurent Amsaleg 2
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. Dramatic performance gains are obtained using approximate search schemes, such as the popular Locality-Sensitive Hashing (LSH). Several extensions have been proposed to address the limitations of this algorithm, in particular, by choosing more appropriate hash functions to better partition the vector space. All the proposed extensions, however, rely on a structured quantizer for hashing, poorly fitting real data sets, limiting its performance in practice. In this paper, we compare several families of space hashing functions in a real setup, namely when searching for high-dimensional SIFT descriptors. The comparison of random projections, lattice quantizers, k-means and hierarchical k-means reveal that unstructured quantizer significantly improves the accuracy of LSH, as it closely fits the data in the feature space. We then compare two querying mechanisms introduced in the literature with the one originally proposed in LSH, and discuss their respective merits and limitations.
Complete list of metadata

Cited literature [29 references]  Display  Hide  Download

Contributor : Hervé Jégou Connect in order to contact the contributor
Submitted on : Friday, February 18, 2011 - 4:34:49 PM
Last modification on : Wednesday, April 27, 2022 - 3:44:04 AM
Long-term archiving on: : Tuesday, November 6, 2012 - 2:20:32 PM


Files produced by the author(s)



Loïc Paulevé, Hervé Jégou, Laurent Amsaleg. Locality sensitive hashing: a comparison of hash function types and querying mechanisms. Pattern Recognition Letters, Elsevier, 2010, 31 (11), pp.1348-1358. ⟨10.1016/j.patrec.2010.04.004⟩. ⟨inria-00567191⟩



Record views


Files downloads