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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.
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Submitted on : Friday, February 18, 2011 - 4:34:49 PM
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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⟩



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