The DOI matches its Crossref record.
Chabot: retrieval from a relational database of images
Abstract
Selecting from a large, expanding collection of images requires carefully chosen search criteria. We present an approach that integrates a relational database retrieval system with a color analysis technique. The Chabot project was initiated at our university to study storage and retrieval of a vast collection of digitized images. These images are from the State of California Department of Water Resources. The goal was to integrate a relational database retrieval system with content analysis techniques that would give our querying system a better method for handling images. Our simple color analysis method, if used in conjunction with other search criteria, improves our ability to retrieve images efficiently. The best result is obtained when text-based search criteria are combined with content-based criteria and when a coarse granularity is used for content analysis.>
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
A survey of content-based image retrieval with high-level semantics
Liu, Y., Zhang, D., Lu, G., & Ma, W.-Y. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2006.04.045 · Cited by 1,696
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
Comparing images using color coherence vectors
Pass, G., Zabih, R., & Miller, J. (1996) · Proceedings of the fourth ACM international conference on Multimedia - MULTIMEDIA '96 · doi:10.1145/244130.244148 · Cited by 837
Belongie, S., Carson, C., Greenspan, H., & Malik, J. (2002) · Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271) · doi:10.1109/iccv.1998.710790 · Cited by 474
NoScope: optimizing neural network queries over video at scale
Kang, D., Emmons, J., Abuzaid, F., Bailis, P., & Zaharia, M. (2017) · Proceedings of the VLDB Endowment · doi:10.14778/3137628.3137664 · Cited by 367
Metadata
- Authors
- Ogle, V. E., & Stonebraker, M.
- Venue
- Computer
- Published
- 1995
- Volume
- 28
- Issue
- 9
- Pages
- 40-48
- ISSN
- 0018-9162 (ISSN-L)
- OpenAlex
- W2102475035
- Cited by
- 650
- References
- 0
- FWCI
- 47.61
- 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: Chabot: retrieval from a relational database of images. https://app.cochat.ai/papers/10.1109/2.410150
- The paper
- Ogle, V. E., & Stonebraker, M. (1995). Chabot: retrieval from a relational database of images. Computer. https://doi.org/10.1109/2.410150
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.
