Social Image Tagging by Mining Sparse Tag Patterns from Auxiliary Data

User-given tags associated with social images from photo-sharing 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 co-occurrence 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 $l_1$ 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.