Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/79987
Title: Learning sparse tag patterns for social image classification
Authors: Duan, Ling-Yu
Yuan, Junsong
Li, Qingyong
Luo, Siwei
Lin, Jie
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2012
Source: Lin, 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.
Abstract: User-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.
URI: https://hdl.handle.net/10356/79987
http://hdl.handle.net/10220/17899
DOI: 10.1109/icip.2012.6467501
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].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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