The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Similarity Scores Based on Background Samples
Overview
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Registry
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Claims the paper makes
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Self-check
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Citations
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
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
(2012) · Encyclopedia of the Sciences of Learning · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,370
THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS
FISHER, R. A. (1936) · Annals of Eugenics · doi:10.1111/j.1469-1809.1936.tb02137.x · Cited by 15,227
Russell, E. L., Chiang, L. H., & Braatz, R. D. (2000) · Advances in Industrial Control · doi:10.1007/978-1-4471-0409-4_3 · Cited by 7,862
A comparative study of texture measures with classification based on featured distributions
Ojala, T., Pietikäinen, M., & Harwood, D. (1996) · Pattern Recognition · doi:10.1016/0031-3203(95)00067-4 · Cited by 6,882
Shape matching and object recognition using shape contexts
Belongie, S., Malik, J., & Puzicha, J. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.993558 · Cited by 6,306
Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments
Huang, G. B., Mattar, M., Berg, T. L., & Miller, E. G. (2008) · HAL · Cited by 4,445
(2006) · The MIT Press eBooks · doi:10.7551/mitpress/9780262033589.001.0001 · Cited by 4,280
Distance Metric Learning for Large Margin Nearest Neighbor Classification
Weinberger, K. Q., & Saul, L. K. (2009) · Journal of Machine Learning Research · doi:10.5555/1577069.1577078 · Cited by 4,044
Cited by
(2010) · International Conference on Machine Learning · Cited by 13,995
Face recognition in unconstrained videos with matched background similarity
Wolf, L., Hassner, T., & Maoz, I. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995566 · Cited by 1,382
Self-citationEffective face frontalization in unconstrained images
Hassner, T., Harel, S., Paz, E., & Enbar, R. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299058 · Cited by 613
Self-citationLearning Locally-Adaptive Decision Functions for Person Verification
Li, Z., Chang, S., Liang, F., Huang, T. S., Cao, L., & Smith, J. R. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.463 · Cited by 466
Juefei-Xu, F., Luu, K., & Savvides, M. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2468173 · Cited by 74
Metadata
- Authors
- Wolf, L., Hassner, T., & Taigman, Y.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2010
- Pages
- 88-97
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1509928947
- Cited by
- 279
- References
- 34
- FWCI
- 24.47
- 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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Similarity Scores Based on Background Samples. https://app.cochat.ai/papers/10.1007/978-3-642-12304-7_9
- The paper
- Wolf, L., Hassner, T., & Taigman, Y. (2010). Similarity Scores Based on Background Samples. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-12304-7_9
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.
