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A survey of content-based image retrieval with high-level semantics
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“The performance of a CBIR system mainly depends on these selected features [7].”
From Medical image retrieval using deep convolutional neural network (2017) · cites this paper as [7]
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Chen, Z., & Zhu, B. (2002) · Information Retrieval · doi:10.1023/a:1012730924277 · Cited by 16
A curve evolution approach for image segmentation using adaptive flows
Feng, H., Castañón, D. A., & Karl, W. C. (2002) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2001.937666 · Cited by 16
IMAGE RETRIEVAL USING SPATIAL CONTEXT
Ren, W., Singh, M., & Singh, S. K. (2002) · doi:10.1142/9789812776266_0004 · Cited by 12
Triangle-inequality-based pruning algorithms with triangle tries
Berman, A. P., & Shapiro, L. G. (1998) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.333855 · Cited by 12
Incorporate discriminant analysis with EM algorithm in image retrieval
Tian, Q. C., Wu, Y., & Huang, T. S. (2002) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2000.869600 · Cited by 12
Colour-based relevance feedback for image retrieval
Low, W.-C., & Chua, T. (2002) · doi:10.1109/mmdbms.1998.709521 · Cited by 11
Learning image similarities and categories from content analysis and relevance feedback
Yang, Z., & Kuo, C. ‐. J. (2000) · doi:10.1145/357744.357927 · Cited by 10
Some Formal Analysis of Roccio's Similarity-Based Relvance Feedback Algorithm
Chen, Z., & Zhu, B. (2000) · International Symposium on Algorithms and Computation · Cited by 9
Spectrally Layered Color Indexing
Qiu, G., & Lam, K. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45479-9_11 · Cited by 9
Central Object Extraction for Object-Based Retrieval.
Kim, S., Park, S.-Y., & Kim, M. H. (2003) · Conference on Image and Video Retrieval · Cited by 5
Texture Retrieval with Linguistic Descriptions
Chiu, C., Lin, H.-C., & Yang, S.-N. (2001) · Lecture notes in computer science · doi:10.1007/3-540-45453-5_40 · Cited by 5
On the Complexity of Rocchio’s Similarity-Based Relevance Feedback Algorithm
Chen, Z., & Fu, B. (2005) · Lecture notes in computer science · doi:10.1007/11602613_23 · Cited by 3
Object class recognition using images of abstract regions
Li, Y., Bilmes, J. A., & Shapiro, L. G. (2004) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2004.1334000 · Cited by 3
The design of end-to-end optimal image retrieval systems
Vasconcelos, N. M. (2003) · Cited by 2
Flagged first, then by citations
15 of the 156 references couldn’t be matched to a known paper.
Research works printed but not linked: 8
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Cited by
Sorted by citations
What Do Different Evaluation Metrics Tell Us About Saliency Models?
Bylinskii, Z., Judd, T., Oliva, A., Torralba, A. B., & Durand, F. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2815601 · Cited by 910
Processing Social Media Messages in Mass Emergency: A Survey
Imran, M., Castillo, C., Diaz, F., & Vieweg, S. (2015) · ACM Computing Surveys · doi:10.1145/2771588 · Cited by 804
“In addition, there are cases of websites being set up in response to crises in 2003 [Palen and Liu 2007].”
BackgroundLocal Tetra Patterns: A New Feature Descriptor for Content-Based Image Retrieval
Murala, S., Maheshwari, R. P., & Raman, B. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2188809 · Cited by 680
“A comprehensive and extensive literature survey on CBIR is presented in [1]–[4].”
BackgroundPatternNet: 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
Understanding Deep Learning Techniques for Image Segmentation
Ghosh, S., Das, N., Das, I., & Maulik, U. (2019) · ACM Computing Surveys · doi:10.1145/3329784 · Cited by 517
“Image segmentation is useful in many cases as they are representative of spatial relations among various objects [12, 127].”
Background
Metadata
- Authors
- Liu, Y., Zhang, D., Lu, G., & Ma, W.-Y.
- Affiliations
- Monash University (AU)
- Microsoft Research Asia (China) (CN)
- Corresponding author
- Liu, Ying
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2007-01
- Volume
- 40
- Issue
- 1
- Pages
- 262-282
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W2046589280
- Cited by
- 1,696
- References
- 156
- Citations by year
- 2026: 12 · 2025: 36 · 2024: 37 · 2023: 49 · 2022: 44 · 2021: 73 · 2020: 73 · 2019: 91 · 2018: 109 · 2017: 132 · 2016: 129 · 2015: 137 · 2014: 138 · 2013: 121 · 2012: 130
- FWCI
- 67.65
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Pattern Recognition (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- content-based image retrieval, high-level semantics, semantic gap, low-level feature extraction, similarity measurement, object ontology, relevance feedback, semantic templates, web image search, information retrieval evaluation
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 ›
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