The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Custom Pictorial Structures for Re-identification
Abstract
We propose a novel methodology for re-identification, based on Pictorial Structures (PS). Whenever face or other biometric information is missing, humans recognize an individual by selectively focusing on the body parts, looking for part-to-part correspondences. We want to take inspiration from this strategy in a re-identification context, using PS to achieve this objective. For single image re-identification, we adopt PS to localize the parts, extract and match their descriptors. When multiple images of a single individual are available, we propose a new algorithm to customize the fit of PS on that specific person, leading to what we call a Custom Pictorial Structure (CPS). CPS learns the appearance of an individual, improving the localization of its parts, thus obtaining more reliable visual characteristics for re-identification. It is based on the statistical learning of pixel attributes collected through spatio-temporal reasoning. The use of PS and CPS leads to state-of-the-art results on all the available public benchmarks, and opens a fresh new direction for research on re-identification.
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.
Integrity signals
Registry
Cites
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.
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
Mean shift: a robust approach toward feature space analysis
Comaniciu, D., & Meer, P. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.1000236 · Cited by 11,305
Yilmaz, A., Javed, O., & Shah, M. (2006) · ACM Computing Surveys · doi:10.1145/1177352.1177355 · Cited by 4,792
Pictorial Structures for Object Recognition
Felzenszwalb, P. F., & Huttenlocher, D. P. (2005) · International Journal of Computer Vision · doi:10.1023/b:visi.0000042934.15159.49 · Cited by 2,180
Person re-identification by symmetry-driven accumulation of local features
Farenzena, M., Bazzani, L., Perina, A., Murino, V., & Cristani, M. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539926 · Cited by 1,499
Self-citationViewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features
Gray, D., & Tao, H. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_21 · Cited by 1,466
A Boosted Particle Filter: Multitarget Detection and Tracking
Okuma, K., Taleghani, A., de Freitas, N., Little, J. J., & Lowe, D. G. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24670-1_3 · Cited by 1,019
Pictorial structures revisited: People detection and articulated pose estimation
Andriluka, M., Roth, S., & Schiele, B. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206754 · Cited by 799
Person Re-Identification by Support Vector Ranking
Prosser, B., Zheng, W.-S., Gong, S., & Xiang, T. (2010) · Procedings of the British Machine Vision Conference 2010 · doi:10.5244/c.24.21 · Cited by 657
Progressive search space reduction for human pose estimation
Ferrari, V., Marin-Jimenez, M., & Zisserman, A. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587468 · Cited by 612
Person Reidentification Using Spatiotemporal Appearance
Gheissari, N., Sebastian, T. B., & Hartley, R. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.223 · Cited by 552
Cited by
Person re-identification by Local Maximal Occurrence representation and metric learning
Liao, S., Hu, Y., Zhu, X., & Li, S. Z. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298832 · Cited by 2,091
Person Transfer GAN to Bridge Domain Gap for Person Re-identification
Wei, L., Zhang, S., Gao, W., & Tian, Q. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00016 · Cited by 2,118
Features for Multi-target Multi-camera Tracking and Re-identification
Ristani, E., & Tomasi, C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00632 · Cited by 556
Person Re-identification by Salience Matching
Zhao, R., Ouyang, W., & Wang, X. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.314 · Cited by 466
Learning 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
Metadata
- Authors
- Cheng, D. S., Cristani, M., Stoppa, M., Bazzani, L., & Murino, V.
- Venue
- Procedings of the British Machine Vision Conference 2011
- Publisher
- British Machine Vision Association
- Published
- 2011
- Pages
- 68.1-68.11
- OpenAlex
- W2126791727
- Cited by
- 616
- References
- 22
- FWCI
- 25.35
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Custom Pictorial Structures for Re-identification. https://app.cochat.ai/papers/10.5244/c.25.68
- The paper
- Cheng, D. S., Cristani, M., Stoppa, M., Bazzani, L., & Murino, V. (2011). Custom Pictorial Structures for Re-identification. Procedings of the British Machine Vision Conference 2011. https://doi.org/10.5244/c.25.68
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.
