Please use this identifier to cite or link to this item: https://doi.org/10.1145/1646396.1646452
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dc.titleNUS-WIDE: A real-world web image database from National University of Singapore
dc.contributor.authorChua, T.-S.
dc.contributor.authorTang, J.
dc.contributor.authorHong, R.
dc.contributor.authorLi, H.
dc.contributor.authorLuo, Z.
dc.contributor.authorZheng, Y.
dc.date.accessioned2013-07-04T08:14:41Z
dc.date.available2013-07-04T08:14:41Z
dc.date.issued2009
dc.identifier.citationChua, T.-S.,Tang, J.,Hong, R.,Li, H.,Luo, Z.,Zheng, Y. (2009). NUS-WIDE: A real-world web image database from National University of Singapore. CIVR 2009 - Proceedings of the ACM International Conference on Image and Video Retrieval : 368-375. ScholarBank@NUS Repository. <a href="https://doi.org/10.1145/1646396.1646452" target="_blank">https://doi.org/10.1145/1646396.1646452</a>
dc.identifier.isbn9781605584805
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/40888
dc.description.abstractThis 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. Copyright 2009 ACM.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1145/1646396.1646452
dc.sourceScopus
dc.subjectAnnotation
dc.subjectFlickr
dc.subjectRetrieval
dc.subjectTag refinement
dc.subjectTraining set construction
dc.subjectWeb image
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1145/1646396.1646452
dc.description.sourcetitleCIVR 2009 - Proceedings of the ACM International Conference on Image and Video Retrieval
dc.description.page368-375
dc.identifier.isiutNOT_IN_WOS
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