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Cited by 2,956Advanced Image and Video Retrieval TechniquesImage Retrieval and Classification TechniquesAdvanced Data Compression Techniques

The DOI matches its Crossref record; 31 of the 35 references it cites were found in a registry or an index.

Product Quantization for Nearest Neighbor Search

Open access
Jégou, H, Douze, M, Schmid, C·2011·IEEE Transactions on Pattern Analysis and Machine Intelligence
DOI 10.1109/tpami.2010.57
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Abstract

This paper introduces a product quantization-based approach for approximate nearest neighbor search. The idea is to decompose the space into a Cartesian product of low-dimensional subspaces and to quantize each subspace separately. A vector is represented by a short code composed of its subspace quantization indices. The euclidean distance between two vectors can be efficiently estimated from their codes. An asymmetric version increases precision, as it computes the approximate distance between a vector and a code. Experimental results show that our approach searches for nearest neighbors efficiently, in particular in combination with an inverted file system. Results for SIFT and GIST image descriptors show excellent search accuracy, outperforming three state-of-the-art approaches. The scalability of our approach is validated on a data set of two billion vectors.

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31 of 31 references verified0 citation errors · 0 not checkable

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Citations

Cites

Show all 35 references

Research works printed but not linked: 2

  1. Weber, A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces, 194
  2. Knuth, 1998, The Art of Computer Programming, Sorting and Searching, 3

Cited by

Sorted by citations

  • Deep Residual Learning for Image Recognition

    He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474

    “For vector quantization, encoding residual vectors [17] is shown to be more effective than encoding original vectors.”
    Background

  • Deep Residual Learning for Image Recognition

    He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196

    “For vector quantization, encoding residual vectors [17] is shown to be more effective than encoding original vectors.”
    Background

  • 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

    “ing a minimal accuracy loss results in orders of magnitude of compression [21]. The most popular vector compression methods can be classied into either binary codes [18, 22], or quantization methods [25, 37]. Both have the desirable property that searching neighbors does not require reconstructing the vectors. Our pap…”
    BackgroundMethodInfluentialSelf-citation

  • Aggregating local descriptors into a compact image representation

    Jeǵou, H., Douze, M., Schmid, C., & Pérez, P. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5540039 · Cited by 2,679

    Self-citation

  • NetVLAD: CNN Architecture for Weakly Supervised Place Recognition

    Arandjelović, R., Gronát, P., Torii, A., Pajdla, T., & Šivic, J. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/cvpr.2016.572 · Cited by 3,532

    “e vector representation for the entire image such as bag-of-visualwords [53,74], VLAD [3,29] or Fisher vector [31,52]. The resulting representation is then usually compressed and effi- ciently indexed [28,74]. The image database can be further augmented by 3D structure that enables recovery of accurate camera pose [40,…”
    Background

See all 2,956

Related

Metadata

Authors
Jégou, H., Douze, M., & Schmid, C.
Affiliations
Institut national de recherche en sciences et technologies du numérique (FR)
ORCID
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2011-01
Volume
33
Issue
1
Pages
117-128
ISSN
0162-8828 (ISSN-L)
DOI
10.1109/tpami.2010.57
OpenAlex
W2124509324
Semantic Scholar
4748d22348e72e6e06c2476486afddbc76e5eca7
Cited by
2,956
References
35
Citations by year
2026: 260 · 2025: 295 · 2024: 233 · 2023: 211 · 2022: 195 · 2021: 204 · 2020: 187 · 2019: 207 · 2018: 201 · 2017: 184 · 2016: 196 · 2015: 196 · 2014: 159 · 2013: 116 · 2012: 71
FWCI
84.24
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
  • PubMed (publishedVersion)
  • HAL (submittedVersion, other-oa)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Data Compression Techniques
Keywords
product quantization, approximate nearest neighbor search, subspace decomposition, inverted index, SIFT descriptors, GIST descriptors, image retrieval, scalability
MeSH terms
Algorithms, Artificial Intelligence, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Pattern Recognition, Automated, Models, Statistical, Cluster Analysis, Information Storage and Retrieval

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