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Cited by 3,579

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Scalable Recognition with a Vocabulary Tree

Nister, D., Stewenius, H.·2006·2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06)
DOI 10.1109/cvpr.2006.264
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Abstract

A recognition scheme that scales efficiently to a large number of objects is presented. The efficiency and quality is exhibited in a live demonstration that recognizes CD-covers from a database of 40000 images of popular music CD’s. The scheme builds upon popular techniques of indexing descriptors extracted from local regions, and is robust to background clutter and occlusion. The local region descriptors are hierarchically quantized in a vocabulary tree. The vocabulary tree allows a larger and more discriminatory vocabulary to be used efficiently, which we show experimentally leads to a dramatic improvement in retrieval quality. The most significant property of the scheme is that the tree directly defines the quantization. The quantization and the indexing are therefore fully integrated, essentially being one and the same. The recognition quality is evaluated through retrieval on a database with ground truth, showing the power of the vocabulary tree approach, going as high as 1 million images.

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Research works printed but not linked: 12

  1. 10.1023/B:VISI.0000020671.28016.e8 · DOI · Paper page
  2. 10.1007/s11263-005-3674-1 · DOI · Paper page
  3. 10.1023/A:1008199403446 · DOI · Paper page
  4. 10.5244/C.19.3 · DOI
  5. 10.1145/997817.997857 · DOI · Paper page
  6. 10.1109/CVPR.2005.320 · DOI · Paper page
  7. 10.1109/CVPR.2000.855899 · DOI · Paper page
  8. 10.1016/j.imavis.2004.02.006 · DOI · Paper page
  9. 10.1109/CVPR.2005.288 · DOI · Paper page
  10. 10.1109/ICCV.2005.239 · DOI · Paper page
  11. fraundorfer, 2004, Evaluation of local detectors on non-planar scenes, Proc 28th Workshop of the Austrian Association for Pattern Recognition, 125
  12. lindeberg, 1994, Shape-adapted smoothing in estimation of 3-d depth cues from affine distortions of local 2-d brightness structure, ECCV, 389

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Metadata

Authors
Nister, D., & Stewenius, H.
Venue
2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06)
Publisher
IEEE
Published
2006-07-10
Volume
2
Pages
2161-2168
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2006.264
OpenAlex
W2128017662
Cited by
3,579
References
6
FWCI
116.49
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Scalable Recognition with a Vocabulary Tree. https://app.cochat.ai/papers/10.1109/cvpr.2006.264
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Nister, D., & Stewenius, H. (2006). Scalable Recognition with a Vocabulary Tree. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06). https://doi.org/10.1109/cvpr.2006.264

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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