The DOI matches its Crossref record.
The MIR flickr retrieval evaluation
Abstract
In most well known image retrieval test sets, the imagery typically cannot be freely distributed or is not representative of a large community of users. In this paper we present a collection for the MIR community comprising 25000 images from the Flickr website which are redistributable for research purposes and represent a real community of users both in the image content and image tags. We have extracted the tags and EXIF image metadata, and also make all of these publicly available. In addition we discuss several challenges for benchmarking retrieval and classification methods.
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
Cited by
Large-Scale Cross-Modality Search via Collective Matrix Factorization Hashing
Ding, G., Guo, Y., Zhou, J., & Gao, Y. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2607421 · Cited by 218
Unsupervised Contrastive Cross-modal Hashing
Hu, P., Zhu, H., Lin, J., Peng, D., Zhao, Y.-P., & Peng, X. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3177356 · Cited by 199
Celard, P., Iglesias, E. L., Sorribes-Fdez, J. M., Romero, R., Vieira, A. S., & Borrajo, L. (2023) · Neural Computing and Applications · doi:10.1007/s00521-022-07953-4 · Cited by 188
Multi-pathway generative adversarial hashing for unsupervised cross-modal retrieval
Zhang, J., & Peng, Y. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2922128 · Cited by 103
Do We Train on Test Data? Purging CIFAR of Near-Duplicates
Barz, B., & Denzler, J. (2020) · Journal of Imaging · doi:10.3390/jimaging6060041 · Cited by 95
Metadata
- Authors
- Huiskes, M. J., & Lew, M. S.
- Venue
- Proceedings of the 1st ACM international conference on Multimedia information retrieval
- Publisher
- ACM
- Published
- 2008-10-30
- Pages
- 39-43
- OpenAlex
- W2155803963
- Cited by
- 1,836
- References
- 0
- FWCI
- 27.51
- 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: The MIR flickr retrieval evaluation. https://app.cochat.ai/papers/10.1145/1460096.1460104
- The paper
- Huiskes, M. J., & Lew, M. S. (2008). The MIR flickr retrieval evaluation. Proceedings of the 1st ACM international conference on Multimedia information retrieval. https://doi.org/10.1145/1460096.1460104
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.
