Content-based social image retrieval with context regularization

The retrieval and recommendation of social media have provided an immense opportunity to exploit the collective behavior of community users through linked multi-modal data, such as images and tags, where tags provide context information, and images represent visual content. The stability of content information is more reliable than user contributed context information, which was ignored by many existing methods. In this paper, through discovering the latent feature space between visual features and context, we propose a novel approach for social image retrieval by imposing context regularization terms to constraint visual features. The method can effectively reflect the interior visual structure for social image representation. Experimental results on the NUS-WIDEOBJECT dataset demonstrate that the proposed approach obtains competitive performance compared with state-of-the-art methods.