Journal
PATTERN RECOGNITION LETTERS
Volume 31, Issue 11, Pages 1348-1358Publisher
ELSEVIER
DOI: 10.1016/j.patrec.2010.04.004
Keywords
Search methods; Image databases; Quantization; Database searching; Information retrieval; LSH
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Funding
- Quaero
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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-dimension 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. (C) 2010 Elsevier B.V. All rights reserved.
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