Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
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
Open this paper in CoChat — free account

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/cvpr.2006.264

Cites

6 of 6 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

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

Cited by

See all 3,579

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.