The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Comparing images using color coherence vectors
Abstract
Color histograms are used to compare images in many applications. Their advantages are e#ciency, and insensitivity to small changes in camera viewpoint. However, color histograms lack spatial information, and this can cause images with very different appearances to have similar histograms. For example, a picture of fall foliage might contain a large number of scattered red pixels; this could have a similar color histogram to a picture of a single large red object. We describe a histogram-based method for comparing images that incorporates spatial information. While a color histogram counts the number of pixels with a given color, a color coherence vector #CCV# measures the spatial coherence of the pixels with a given color. If the red pixels in an image are members of large red regions, this color will have high coherence, while if the red pixels are widely scattered it will havelow coherence. CCV's can be computed at over 5 images per second on a standard workstation. A da...
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
Swain, M. J., & Ballard, D. H. (1991) · International Journal of Computer Vision · doi:10.1007/bf00130487 · Cited by 5,623
Land, E. H., & McCann, J. J. (1971) · Journal of the Optical Society of America · doi:10.1364/josa.61.000001 · Cited by 4,095
Photobook: Content-based manipulation of image databases
Pentland, A., Picard, R. W., & Sclaroff, S. (1996) · International Journal of Computer Vision · doi:10.1007/bf00123143 · Cited by 1,562
Query by Image and Video Content: The QBIC System
Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Huang, Q., Dom, B., Gorkani, M., Hafner, J., Lee, D., Petkovic, D., et al. (2002) · Readings in Multimedia Computing and Networking · doi:10.1016/b978-155860651-7/50108-x · Cited by 1,559
Automatic partitioning of full-motion video
Zhang, H., Kankanhalli, A., & Smoliar, S. W. (1993) · Multimedia Systems · doi:10.1007/bf01210504 · Cited by 1,274
Efficient color histogram indexing for quadratic form distance functions
Hafner, J., Sawhney, H. S., Equitz, W., Flickner, M., & Niblack, W. (1995) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.391417 · Cited by 833
Chabot: retrieval from a relational database of images
Ogle, V. E., & Stonebraker, M. (1995) · Computer · doi:10.1109/2.410150 · Cited by 650
Funt, B., & Finlayson, G. D. (1995) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.391390 · Cited by 560
Tools and techniques for color image retrieval:
Smith, J. R., & Chang, S.-F. (1996) · SPIE Proceedings · doi:10.1117/12.234781 · Cited by 466
Color indexing with weak spatial constraints:
Stricker, M. A., & Dimai, A. (1996) · SPIE Proceedings · doi:10.1117/12.234802 · Cited by 232
Cited by
Fast implementation of real-time fruit detection in apple orchards using deep learning
Kang, H., & Chen, C. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.105108 · Cited by 243
Design and Development of a Multimodal Biomedical Information Retrieval System
Demner-Fushman, D., Antani, S., Simpson, M., & Thoma, G. R. (2012) · Journal of Computing Science and Engineering · doi:10.5626/jcse.2012.6.2.168 · Cited by 161
Metadata
- Authors
- Pass, G., Zabih, R., & Miller, J.
- Venue
- Proceedings of the fourth ACM international conference on Multimedia - MULTIMEDIA '96
- Publisher
- ACM Press
- Published
- 1996
- Pages
- 65-73
- OpenAlex
- W2105568170
- Cited by
- 837
- References
- 13
- FWCI
- 15.40
- 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: Comparing images using color coherence vectors. https://app.cochat.ai/papers/10.1145/244130.244148
- The paper
- Pass, G., Zabih, R., & Miller, J. (1996). Comparing images using color coherence vectors. Proceedings of the fourth ACM international conference on Multimedia - MULTIMEDIA '96. https://doi.org/10.1145/244130.244148
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.
