Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/79987
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dc.contributor.authorDuan, Ling-Yuen
dc.contributor.authorYuan, Junsongen
dc.contributor.authorLi, Qingyongen
dc.contributor.authorLuo, Siweien
dc.contributor.authorLin, Jieen
dc.date.accessioned2013-11-29T03:31:26Zen
dc.date.accessioned2019-12-06T13:38:13Z-
dc.date.available2013-11-29T03:31:26Zen
dc.date.available2019-12-06T13:38:13Z-
dc.date.copyright2012en
dc.date.issued2012en
dc.identifier.citationLin, J., Duan, L.-Y., Yuan, J., Li, Q., & Luo, S. (2012). Learning sparse tag patterns for social image classification. 19th IEEE International Conference on Image Processing (ICIP), 2881-2884.en
dc.identifier.urihttps://hdl.handle.net/10356/79987-
dc.description.abstractUser-generated tags associated with images from social media (e.g., Flickr) provide valuable textual resources for image classification. However, the noisy and huge tag vocabulary heavily degrades the effectiveness and efficiency of state-of-the-art image classification methods that exploited auxiliary web data. To alleviate the problem, we introduce a Sparse Tag Patterns (STP) model to discover sparsity constrained co-occurrence tag patterns from large scale user contributed tags among social data. To fulfill the compactness and discriminability, we formulate STP as a problem of minimizing a quadratic loss function regularized by the bi-layer l1 norm. We treat the learned STP as alternative intermediate semantic image feature and verify its superiority within a search-based image classification framework. Experiments on 240K social images associated with millions of tags have demonstrated encouraging performance of the proposed method compared to the state-of-the-art.en
dc.format.extent4 p. This work was supported in part by the National Basic Research Program of China (2009CB320902), in part by grants from the National Science Foundation of China (60902057 and 61121002) and Nanyang Assistant Professorship SUG M4080134.en
dc.language.isoenen
dc.rights© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/icip.2012.6467501].en
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen
dc.titleLearning sparse tag patterns for social image classificationen
dc.typeConference Paperen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.contributor.conferenceIEEE International Conference on Image Processing (19th : 2012 : Orlando, Florida, US)en
dc.identifier.doi10.1109/icip.2012.6467501en
dc.description.versionAccepted versionen
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