The DOI matches its Crossref record.
Content based image retrieval systems
Abstract
Images are being generated at an ever-increasing rate by sources such as defence and civilian satellites, military reconnaissance and surveillance flights, fingerprinting and mug-shot-capturing devices, scientific experiments, biomedical imaging, and home entertainment systems. For example, NASA's Earth Observing System will generate about 1 terabyte of image data per day when fully operational. A content-based image retrieval (CBIR) system is required to effectively and efficiently use information from these image repositories. Such a system helps users (even those unfamiliar with the database) retrieve relevant images based on their contents. Application areas in which CBIR is a principal activity are numerous and diverse. With the recent interest in multimedia systems, CBIR has attracted the attention of researchers across several disciplines.>
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
Image indexing using color correlograms
Huang, J., Kumar, S. R., Mitra, M., Zhu, W.-J., & Zabih, R. (2002) · Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1997.609412 · Cited by 1,646
Spatial Color Indexing and Applications
Huang, J., Ravi Kumar, S., Mitra, M., Zhu, W.-J., & Zabih, R. (1999) · International Journal of Computer Vision · doi:10.1023/a:1008108327226 · Cited by 271
VFIRM: Verifiable Fine-Grained Encrypted Image Retrieval in Multi-owner Multi-user Settings
Tong, Q., Miao, Y., Chen, L., Weng, J., Liu, X., Choo, K.-K. R., & Deng, R. H. (2022) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2021.3083512 · Cited by 58
Metadata
- Authors
- Gudivada, V. N., & Raghavan, V. V.
- Venue
- Computer
- Published
- 1995
- Volume
- 28
- Issue
- 9
- Pages
- 18-22
- ISSN
- 0018-9162 (ISSN-L)
- OpenAlex
- W2132201434
- Cited by
- 875
- References
- 0
- FWCI
- 36.06
- Type
- journal article
- 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: Content based image retrieval systems. https://app.cochat.ai/papers/10.1109/2.410145
- The paper
- Gudivada, V. N., & Raghavan, V. V. (1995). Content based image retrieval systems. Computer. https://doi.org/10.1109/2.410145
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.
