NUS-WIDE: a real-world web image database from National University of Singapore
Abstract
This paper introduces a web image dataset created by NUS's Lab for Media Search. The dataset includes: (1) 269,648 images and the associated tags from Flickr, with a total of 5,018 unique tags; (2) six types of low-level features extracted from these images, including 64-D color histogram, 144-D color correlogram, 73-D edge direction histogram, 128-D wavelet texture, 225-D block-wise color moments extracted over 5x5 fixed grid partitions, and 500-D bag of words based on SIFT descriptions; and (3) ground-truth for 81 concepts that can be used for evaluation. Based on this dataset, we highlight characteristics of Web image collections and identify four research issues on web image annotation and retrieval. We also provide the baseline results for web image annotation by learning from the tags using the traditional k-NN algorithm. The benchmark results indicate that it is possible to learn effective models from sufficiently large image dataset to facilitate general image retrieval.
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These general image datasets have a large number of pictures and rich types, but their image resolution is below 500×500, which does not meet the needs of operations such as blurring, scaling and watermark.
“These datasets have a large number of pictures and rich types, but the images resolution below 500 500, which does not meet the needs of operations such as blurring, scaling and watermark.”
From Deep Perceptual Hash Based on Hash Center for Image Copyright Protection (2022) · cites this paper as [41]
Previous studies have generally transformed and generated training sets on the basis of general image datasets such as ImageNet [40], NUS WIDE [41] and MS COCO [42].
“Previous studies have generally transformed and generated training sets on the basis of general image datasets such as ImageNet [40], NUS WIDE [41] and MS COCO [42].”
From Deep Perceptual Hash Based on Hash Center for Image Copyright Protection (2022) · cites this paper as [41]
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589 · see citing works
Texture features for browsing and retrieval of image data
Manjunath, B. S., & Ma, W. (1996) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.531803 · Cited by 3,729 · see citing works
Fei-Fei, L., Fergus, R., & Perona, P. (2007) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2005.09.012 · Cited by 2,359 · see citing works
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Arya, S., Mount, D. M., Netanyahu, N. S., Silverman, R., & Wu, A. Y. (1998) · Journal of the ACM · doi:10.1145/293347.293348 · Cited by 2,290 · see citing works
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
Torralba, A., Fergus, R., & Freeman, W. T. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.128 · Cited by 1,921 · see citing works
Stricker, M. A., & Orengo, M. (1995) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.205308 · Cited by 1,749 · see citing works
Stockman, G. C., & Shapiro, L. G. (2001) · Cited by 1,706 · see citing works
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 · see citing works
Large-Scale Concept Ontology for Multimedia
Naphade, M., Smith, J. R., Tešić, J., Chang, S.-F., Hsu, W. H., Kennedy, L., Hauptmann, A. G., & Curtis, J. (2006) · IEEE Multimedia · doi:10.1109/mmul.2006.63 · Cited by 648 · see citing works
The challenge problem for automated detection of 101 semantic concepts in multimedia
Snoek, C. G. M., Worring, M., van Gemert, J. C., Geusebroek, J., & Smeulders, A. W. M. (2006) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1180639.1180727 · Cited by 621 · see citing works
An optimal algorithm for approximate nearest neighbor searching
Arya, S., Mount, D. M., Netanyahu, N. S., Silverman, R., & Wu, A. Y. (1994) · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · doi:10.5555/314464.314652 · Cited by 590 · see citing works
Correlative multi-label video annotation
Qi, G.-J., Hua, X., Rui, Y., et al. (2007) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1291233.1291245 · Cited by 478 · see citing works
Efficient use of local edge histogram descriptor
Park, D. K., Jeon, Y. S., & Won, C. S. (2000) · doi:10.1145/357744.357758 · Cited by 256 · see citing works
Annotating Images by Mining Image Search Results
Wang, X., Zhang, L., Li, X., et al. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.127 · Cited by 198 · see citing works
Can High-Level Concepts Fill the Semantic Gap in Video Retrieval? A Case Study With Broadcast News
Hauptmann, A. G., Yan, R., Lin, W., Christel, M. G., & Wactlar, H. D. (2007) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2007.900150 · Cited by 190 · see citing works
Qi, G.-J., Hua, X., Rui, Y., et al. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.218 · Cited by 78 · see citing works
Correlative Linear Neighborhood Propagation for Video Annotation
Tang, J., Hua, X., Wang, M., et al. (2008) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2008.2006045 · Cited by 67 · see citing works
Self-citationWhat are the high-level concepts with small semantic gaps?
Lu, Y., Zhang, L., Tian, Q., & Ma, W. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587827 · Cited by 49 · see citing works
To construct optimal training set for video annotation
Tang, J., Song, Y., Hua, X., et al. (2006) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1180639.1180667 · Cited by 11 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Cui, P., Wang, X., Pei, J., & Zhu, W. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2849727 · Cited by 1,274 · see citing works
Transfer Learning for Visual Categorization: A Survey
Shao, L., Zhu, F., & Li, X. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2330900 · Cited by 895 · see citing works
HashNet: Deep Learning to Hash by Continuation
Cao, Z., Long, M., Wang, J., & Yu, P. S. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.598 · Cited by 777 · see citing works
A secure federated transfer learning framework
Liu, Y., Kang, Y., Xing, C., Chen, T., & Yang, Q. (2020) · IEEE Intelligent Systems · doi:10.1109/mis.2020.2988525 · Cited by 645 · see citing works
Central Similarity Quantization for Efficient Image and Video Retrieval
Yuan, L., Wang, T., Zhang, X., Tay, F. E., Jie, Z., Liu, W., & Feng, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00315 · Cited by 413 · see citing works
Deep Fuzzy Hashing Network for Efficient Image Retrieval
Lu, H., Zhang, M., Xu, X., Li, Y., & Shen, H. T. (2021) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2020.2984991 · Cited by 340 · see citing works
CCL: Cross-modal Correlation Learning With Multigrained Fusion by Hierarchical Network
Peng, Y., Qi, J., Huang, X., & Yuan, Y. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2742704 · Cited by 228 · see citing works
Scalable Deep Multimodal Learning for Cross-Modal Retrieval
Hu, P., Zhen, L., Peng, D., & Liu, P. (2019) · Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3331184.3331213 · Cited by 136 · see citing works
Learning to Hash With Optimized Anchor Embedding for Scalable Retrieval
Guo, Y., Ding, G., Liu, L., Han, J., & Shao, L. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2652730 · Cited by 108 · see citing works
Multi-pathway generative adversarial hashing for unsupervised cross-modal retrieval
Zhang, J., & Peng, Y. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2922128 · Cited by 103 · see citing works
Joint feature synthesis and embedding: Adversarial cross-modal retrieval revisited
Xu, X., Lin, K., Yang, Y., Hanjalic, A., & Shen, H. T. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3045530 · Cited by 88 · see citing works
COOKIE: Contrastive Cross-Modal Knowledge Sharing Pre-training for Vision-Language Representation
Wen, K., Xia, J., Huang, Y., Li, L., Xu, J., & Shao, J. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00221 · Cited by 37 · see citing works
Deep Perceptual Hash Based on Hash Center for Image Copyright Protection
Sun, X., & Zhou, J. (2022) · IEEE Access · doi:10.1109/access.2022.3221980 · Cited by 20 · see citing works
“Previous studies have generally transformed and generated training sets on the basis of general image datasets such as ImageNet [40], NUS WIDE [41] and MS COCO [42].”
BackgroundMethod
- Authors
- Chua, T.-S., Tang, J., Hong, R., Li, H., Luo, Z., & Zheng, Y.
- Affiliations
- National University of Singapore (SG)
- National University of Singapore, Singapore
- Venue
- Proceedings of the ACM International Conference on Image and Video Retrieval
- Publisher
- ACM
- Published
- 2009-07-08
- Pages
- 1-9
- OpenAlex
- W2007972815
- Cited by
- 3,025
- References
- 19
- Citations by year
- 2026: 140 · 2025: 229 · 2024: 244 · 2023: 239 · 2022: 226 · 2021: 262 · 2020: 247 · 2019: 275 · 2018: 212 · 2017: 170 · 2016: 150 · 2015: 171 · 2014: 120 · 2013: 98 · 2012: 104
- FWCI
- 65.83
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Image and Video Retrieval (CIVR) (publishedVersion)
- National University of Singapore (submittedVersion)
- Funders
- National University of Singapore
- 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
- image annotation, image retrieval, Flickr, image tags, low-level image features, color histogram, color correlogram, edge direction histogram, color moments, k-nearest neighbors, media search
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