The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Locality sensitive hashing: A comparison of hash function types and querying mechanisms
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-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.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Video Google: a text retrieval approach to object matching in videos
Sivic, & Zisserman (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238663 · Cited by 6,367
Scale & Affine Invariant Interest Point Detectors
Mikolajczyk, K., & Schmid, C. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000027790.02288.f2 · Cited by 3,436
When Is ''Nearest Neighbor'' Meaningful?
Beyer, K., Goldstein, J., Ramakrishnan, R., & Shaft, U. (1999) · Lecture Notes in Computer Science · doi:10.1007/3-540-49257-7_15 · Cited by 2,403
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
Jegou, H., Douze, M., & Schmid, C. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_24 · Cited by 1,901 · cited 10×
“…functions have recently been proposed to improve the performance of E2LSH, including the Leech lat-tices (Shakhnarovich et al., 2006) and E8 lattices (J´egou et al., 2008a), which o ff er strong quantization properties, and spherical hash functions (Terasawa and Tanaka, 2007) for unit-norm vectors.”
InfluentialSelf-citationNear-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions
Andoni, A., & Indyk, P. (2006) · 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06) · doi:10.1109/focs.2006.49 · Cited by 1,067
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Billion-scale similarity search with GPUs
Johnson, J., Douze, M., & Jegou, H. (2021) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2019.2921572 · Cited by 4,228
Douze, M., Guzhva, A., Deng, C., Johnson, J., Szilvasy, G., Mazaré, P.-E., Lomeli, M., Hosseini, L., & Jégou, H. (2026) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3618474 · Cited by 478
K-Means Hashing: An Affinity-Preserving Quantization Method for Learning Binary Compact Codes
He, K., Wen, F., & Sun, J. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.378 · Cited by 395
Developing and Utilizing a Large-Scale Cantonese Dataset for Multi-Tasking in Large Language Models
Jiang, J., Truong, A. K. Y., Chen, Y., Bao, Q., Wang, S., Chen, P., Wang, J., Kong, L., Li, Y., & Wu, C. (2025) · Findings of the Association for Computational Linguistics: EMNLP 2025 · doi:10.18653/v1/2025.findings-emnlp.102 · Cited by 1
Metadata
- Authors
- Paulevé, L., Jégou, H., & Amsaleg, L.
- Venue
- Pattern Recognition Letters
- Publisher
- Elsevier BV
- Published
- 2010-08
- Volume
- 31
- Issue
- 11
- Pages
- 1348-1358
- ISSN
- 0167-8655 (ISSN-L)
- OpenAlex
- W2059139185
- Cited by
- 297
- References
- 6
- FWCI
- 22.48
- Type
- journal article
- Language
- en
- Open access
- closed
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