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|Title:||Social image tagging by mining sparse tag patterns from auxiliary data||Authors:||Lin, Jie
|Keywords:||DRNTU::Engineering::Electrical and electronic engineering||Issue Date:||2012||Source:||Lin, J., Yuan, J., Duan, L. -Y., Luo, S., & Gao, W. (2012). Social image tagging by mining sparse tag patterns from auxiliary data. IEEE International Conference on Multimedia and Expo (ICME), 7-12.||Abstract:||User-given tags associated with social images from photosharing websites (e.g., Flickr) are valuable auxiliary resources for the image tagging task. However, social images often suffer from noisy and incomplete tags, heavily degrading the effectiveness of previous image tagging approaches. To alleviate the problem, we introduce a Sparse Tag Patterns (STP) model to discover noiseless and complementary cooccurrence tag patterns from large scale user contributed tags among auxiliary web data. To fulfill the compactness and discriminability, we formulate the STP model as a problem of minimizing quadratic loss function regularized by bi-layer ℓ1 norm. We treat the learned STP as a universal knowledge base and verify its superiority within a data-driven image tagging framework. Experimental results over 1 million auxiliary data demonstrate superior performance of the proposed method compared to the state-of-the-art.||URI:||https://hdl.handle.net/10356/99569
|DOI:||http://dx.doi.org/10.1109/ICME.2012.170||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/ICME.2012.170].||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Conference Papers|
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