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Issue No.05 - May (2012 vol.34)
pp: 850-862
Thomas S. Huang , Beckman Inst. for Adv. Sci. & Technol., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA
ABSTRACT
Social media networks contain both content and context-specific information. Most existing methods work with either of the two for the purpose of multimedia mining and retrieval. In reality, both content and context information are rich sources of information for mining, and the full power of mining and processing algorithms can be realized only with the use of a combination of the two. This paper proposes a new algorithm which mines both context and content links in social media networks to discover the underlying latent semantic space. This mapping of the multimedia objects into latent feature vectors enables the use of any off-the-shelf multimedia retrieval algorithms. Compared to the state-of-the-art latent methods in multimedia analysis, this algorithm effectively solves the problem of sparse context links by mining the geometric structure underlying the content links between multimedia objects. Specifically for multimedia annotation, we show that an effective algorithm can be developed to directly construct annotation models by simultaneously leveraging both context and content information based on latent structure between correlated semantic concepts. We conduct experiments on the Flickr data set, which contains user tags linked with images. We illustrate the advantages of our approach over the state-of-the-art multimedia retrieval techniques.
INDEX TERMS
social networking (online), data mining, information retrieval, multimedia systems, user tags, social media networks, context-specific information, content-specific information, multimedia mining, context links, content links, latent semantic space, multimedia objects, latent feature vectors, multimedia retrieval algorithms, geometric structure, multimedia annotation, latent structure, correlated semantic concepts, Flickr data set, Multimedia communication, Context, Semantics, Media, Large scale integration, Context modeling, Visualization, multimedia information networks., Context and content links, latent semantic space, low-rank method, social Media
CITATION
Thomas S. Huang, "Exploring Context and Content Links in Social Media: A Latent Space Method", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.34, no. 5, pp. 850-862, May 2012, doi:10.1109/TPAMI.2011.191
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