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Title: Social image tagging by mining sparse tag patterns from auxiliary data
Authors: Lin, Jie
Yuan, Junsong
Duan, Ling-Yu
Luo, Siwei
Gao, Wen
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.
Conference: IEEE International Conference on Multimedia and Expo (2012 : Melbourne, Australia)
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.
DOI: 10.1109/ICME.2012.170
Schools: School of Electrical and Electronic Engineering 
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: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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