The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Color indexing
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
Aloimonos, J., Weiss, I., & Bandyopadhyay, A. (1988) · International Journal of Computer Vision · doi:10.1007/bf00133571 · Cited by 931
Cited by
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916
Comaniciu, D., Ramesh, V., & Meer, P. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1195991 · Cited by 4,560
LabelMe: A Database and Web-Based Tool for Image Annotation
Russell, B. C., Torralba, A., Murphy, K. P., & Freeman, W. T. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0090-8 · Cited by 4,210
Detecting faces in images: a survey
Yang, M.-H., Kriegman, D. J., & Ahuja, N. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.982883 · Cited by 3,427
Deep Learning for Generic Object Detection: A Survey
Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2018) · arXiv · doi:10.48550/arxiv.1809.02165 · Cited by 2,965
Remote Sensing Image Scene Classification: Benchmark and State of the Art
Cheng, G., Han, J., & Lu, X. (2017) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2675998 · Cited by 2,926
Deep Learning for Generic Object Detection: A Survey
Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01247-4 · Cited by 2,965
When Is ''Nearest Neighbor'' Meaningful?
Beyer, K., Goldstein, J., Ramakrishnan, R., & Shaft, U. (1999) · Lecture Notes in Computer Science · doi:10.1007/3-540-49257-7_15 · Cited by 2,403
AID: A benchmark data set for performance evaluation of aerial scene classification
Xia, G.-S., Hu, J., Hu, F., Shi, B., Bai, X., Zhong, Y., Zhang, L., & Lu, X. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2685945 · Cited by 2,372
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
Modeling visual attention via selective tuning
Tsotsos, J. K., Culhane, S. M., Kei Wai, W. Y., Lai, Y., Davis, N., & Nuflo, F. (1995) · Artificial Intelligence · doi:10.1016/0004-3702(95)00025-9 · Cited by 1,143
Deictic codes for the embodiment of cognition
Ballard, D. H., Hayhoe, M. M., Pook, P. K., & Rao, R. P. N. (1997) · Behavioral and Brain Sciences · doi:10.1017/s0140525x97001611 · Cited by 1,082
Self-citationClassification using intersection kernel support vector machines is efficient
Maji, S., Berg, A. C., & Malik, J. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587630 · Cited by 940
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
Memory Representations in Natural Tasks
Ballard, D. H., Hayhoe, M. M., & Pelz, J. B. (1995) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1995.7.1.66 · Cited by 817
Self-citationZhang, B., Shan, S., Chen, X., & Gao, W. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.884956 · Cited by 580
PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval
Zhou, W., Newsam, S., Li, C., & Shao, Z. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.01.004 · Cited by 571
“al sub-windows. These local descriptors are then used to form a vocabulary of “visual morphological words” through clustering. In [53], SIFT, local binary pattern (LBP) [88], and color histogram (CH) [89] are combined with BOVW to extract mid-level local features. Other works focus on improving the BOVW framework in o…”
BackgroundMethodInfluentialLocal feature view clustering for 3D object recognition
Lowe, D. G. (2005) · Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 · doi:10.1109/cvpr.2001.990541 · Cited by 482
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
Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY
Czimmermann, T., Ciuti, G., Milazzo, M., Chiurazzi, M., Roccella, S., Oddo, C. M., & Dario, P. (2020) · Sensors · doi:10.3390/s20051459 · Cited by 441
Learning Motion Patterns of People for Compliant Robot Motion
Bennewitz, M., Burgard, W., Cielniak, G., & Thrun, S. (2005) · The International Journal of Robotics Research · doi:10.1177/0278364904048962 · Cited by 399
Content-based image retrieval in radiology: current status and future directions
Akgül, C. B., Rubin, D. L., Napel, S., Beaulieu, C. F., Greenspan, H., & Acar, B. (2011) · Journal of Digital Imaging · doi:10.1007/s10278-010-9290-9 · Cited by 388
Empirical evaluation of dissimilarity measures for color and texture
Puzicha, J., Buhmann, J. M., Rubner, Y., & Tomasi, C. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790412 · Cited by 361
Perceptual Metrics for Image Database Navigation
Rubner, Y., & Tomasi, C. (2001) · Springer eBooks · doi:10.1007/978-1-4757-3343-3 · Cited by 296
Sparse coding based visual tracking: Review and experimental comparison
Zhang, S., Yao, H., Sun, X., & Lu, X. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2012.10.006 · Cited by 284
Text detection and recognition in images and video frames
Chen, D., Odobez, J.-M., & Bourlard, H. (2004) · Pattern Recognition · doi:10.1016/j.patcog.2003.06.001 · Cited by 277
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
Efficient use of local edge histogram descriptor
Park, D. K., Jeon, Y. S., & Won, C. S. (2000) · Proceedings of the 2000 ACM workshops on Multimedia · doi:10.1145/357744.357758 · Cited by 255
Diffusion Distance for Histogram Comparison
Ling, H., & Okada, K. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06) · doi:10.1109/cvpr.2006.99 · Cited by 222
Feature Extraction Based on Co-occurrence of Adjacent Local Binary Patterns
Nosaka, R., Ohkawa, Y., & Fukui, K. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-25346-1_8 · Cited by 192
Content-based image copy detection
Kim, C. (2003) · Signal Processing Image Communication · doi:10.1016/s0923-5965(02)00130-3 · Cited by 165
Contribution of color to face recognition
Yip, A. W., & Sinha, P. (2002) · Perception · doi:10.1068/p3376 · Cited by 161
An active contour model based on local fitted images for image segmentation
Wang, L., Chang, Y., Wang, H., Wu, Z., Pu, J., & Yang, X. (2017) · Information Sciences · doi:10.1016/j.ins.2017.06.042 · Cited by 127
Grauman, K., & Leibe, B. (2011) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.2200/s00332ed1v01y201103aim011 · Cited by 126
Motion analysis and segmentation through spatio-temporal slices processing
Ngo, C.-W., Pong, T.-C., & Zhang, H.-J. (2003) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.809020 · Cited by 123
DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data
Fu, S., Tamir, N., Sundaram, S., Chai, L., Zhang, R., Dekel, T., & Isola, P. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-2208 · Cited by 108
Hamming Distributions of Popular Perceptual Hashing Techniques
McKeown, S., & Buchanan, W. J. (2022) · arXiv · doi:10.48550/arxiv.2212.08035 · Cited by 15
Hamming distributions of popular perceptual hashing techniques
McKeown, S., & Buchanan, W. J. (2023) · Forensic Science International: Digital Investigation · doi:10.1016/j.fsidi.2023.301509 · Cited by 15
Hajizadeh, A., Matysiak, A., Brechmann, A., König, R., & May, P. J. C. (2021) · Psychophysiology · doi:10.1111/psyp.13769 · Cited by 9
Wish I hadn't clicked that: context based icons for mobile web navigation and directed search tasks
Setlur, V., Rossoff, S., & Gooch, B. (2011) · Proceedings of the 16th international conference on Intelligent user interfaces · doi:10.1145/1943403.1943429 · Cited by 8
Compact Time-Domain Representation for Logical Access Spoofed Audio
Karo, M., Yeredor, A., & Lapidot, I. (2024) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2023.3341000 · Cited by 6
Beyond Hamming Distance: Exploring spatial encoding in perceptual hashes
McKeown, S. (2025) · Forensic Science International: Digital Investigation · doi:10.1016/j.fsidi.2025.301878 · Cited by 3
Metadata
- Authors
- Swain, M. J., & Ballard, D. H.
- Venue
- International Journal of Computer Vision
- Published
- 1991-11
- Volume
- 7
- Issue
- 1
- Pages
- 11-32
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2914885528
- Cited by
- 5,623
- References
- 1
- FWCI
- 23.58
- 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
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.
