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UCID: an uncompressed color image database:
Abstract
Standardised image databases or rather the lack of them are one of the main weaknesses in the field of content based image retrieval (CBIR). Authors often use their own images or do not specify the source of their datasets. Naturally this makes comparison of results somewhat difficult. While a first approach towards a common colour image set has been taken by the MPEG 7 committee their database does not cater for all strands of research in the CBIR community. In particular as the MPEG-7 images only exist in compressed form it does not allow for an objective evaluation of image retrieval algorithms that operate in the compressed domain or to judge the influence image compression has on the performance of CBIR algorithms. In this paper we introduce a new dataset, UCID (pronounced "use it") - an Uncompressed Colour Image Dataset which tries to bridge this gap. The UCID dataset currently consists of 1338 uncompressed images together with a ground truth of a series of query images with corresponding models that an ideal CBIR algorithm would retrieve. While its initial intention was to provide a dataset for the evaluation of compressed domain algorithms, the UCID database also represents a good benchmark set for the evaluation of any kind of CBIR method as well as an image set that can be used to evaluate image compression and colour quantisation algorithms.
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Cited by
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Removing Rain from a Single Image via Discriminative Sparse Coding
Luo, Y., Xu, Y., & Ji, H. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.388 · Cited by 837
RAISE: a raw images dataset for digital image forensics
Dang-Nguyen, D.-T., Pasquini, C., Conotter, V., & Boato, G. (2015) · Proceedings of the 6th ACM Multimedia Systems Conference · doi:10.1145/2713168.2713194 · Cited by 508
MFC Datasets: Large-Scale Benchmark Datasets for Media Forensic Challenge Evaluation
Guan, H., Kozak, M., Robertson, E., Lee, Y., Yates, A. N., Delgado, A., Zhou, D., Kheyrkhah, T., Smith, J., & Fiscus, J. (2019) · 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw.2019.00018 · Cited by 329
On The Detection of Synthetic Images Generated by Diffusion Models
Corvi, R., Cozzolino, D., Zingarini, G., Poggi, G., Nagano, K., & Verdoliva, L. (2023) · ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp49357.2023.10095167 · Cited by 304
Metadata
- Authors
- Schaefer, G., & Stich, M.
- Venue
- SPIE Proceedings
- Publisher
- SPIE
- Published
- 2003-12-22
- Volume
- 5307
- Pages
- 472-480
- ISSN
- 0277-786X (ISSN-L)
- OpenAlex
- W2096933452
- Cited by
- 1,071
- References
- 0
- FWCI
- 3.95
- Type
- conference paper
- Language
- en
- Open access
- closed
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