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Title: Learning sparse tag patterns for social image classification
Authors: Lin, Jie
Duan, Ling-Yu
Yuan, Junsong
Li, Qingyong
Luo, Siwei
Issue Date: 2012
Conference: IEEE International Conference on Image Processing (19th : 2012 : Orlando, Florida, US)
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.
DOI: 10.1109/ICIP.2012.6467501
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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