Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 3,613

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

A Bayesian Hierarchical Model for Learning Natural Scene Categories

Fei-Fei Li, Perona, P.·2005·2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
DOI 10.1109/cvpr.2005.16
Open this paper in CoChat — free account

Abstract

We propose a novel approach to learn and recognize natural scene categories. Unlike previous work, it does not require experts to annotate the training set. We represent the image of a scene by a collection of local regions, denoted as codewords obtained by unsupervised learning. Each region is represented as part of a "theme". In previous work, such themes were learnt from hand-annotations of experts, while our method learns the theme distributions as well as the codewords distribution over the themes without supervision. We report satisfactory categorization performances on a large set of 13 categories of complex scenes.

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.2005.16

Cites

5 of 5 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

Show all 5 references

Research works printed but not linked: 13

  1. vogel, 2004, A semantic typicality measure for natural scene categorization, DAGM04 Annual Pattern Recognition Symposium, 10.1007/978-3-540-28649-3_24 · DOI
  2. winn, 2003, Variational Message Passing and Its Applications
  3. 10.1109/83.892448 · DOI · Paper page
  4. 10.1109/CVPR.2003.1211534 · DOI · Paper page
  5. 10.1109/CVPR.2004.1315241 · DOI · Paper page
  6. 10.1016/0010-0285(80)90005-5 · DOI · Paper page
  7. 10.1109/CAIVD.1998.646032 · DOI · Paper page
  8. 10.1109/ICPR.1994.576325 · DOI · Paper page
  9. 10.1162/jmlr.2003.3.4-5.993 · DOI · Paper page
  10. 10.1073/pnas.092277599 · DOI · Paper page
  11. 10.1023/A:1012460413855 · DOI · Paper page
  12. oliva, 2001, Modeling the shape of the scene: A holistic representation of the spatial envelope, Int Journal of Computer Vision, 42
  13. minka, 2002, Expectation-propagation for the generative aspect model, Proc Uncertainty in Artificial Intelligence Conference, 352

Cited by

Metadata

Authors
Li, F.-F., & Perona, P.
Venue
2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
Publisher
IEEE
Published
2005-07-27
Volume
2
Pages
524-531
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2005.16
OpenAlex
W2107034620
Cited by
3,613
References
5
FWCI
69.56
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.