Abstract
Social network study has become an important topic in many research fields. Early works on social network analysis focus on real world social interactions in either human society or animal world. With the explosion of Internet data, social network researchers start to pay more attention to the tremendous amount of online social network data. There are ample space for exploring social network research on large-scale online visual content. In this paper, we focus on studying multi-label collective classification problem and develop a model that can harness the mutually beneficial information among the visual appearance, related semantic content and the social network structure simultaneously. Our algorithm is then tested on CelebrityNet, a social network constructed by inferring implicit relationship of people based on online multimedia content. We apply our model to a few important multimedia applications such as image annotation and community classification. We demonstrate that our algorithm significantly outperforms traditional methods on community classification and image annotation.
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Li, LJ., Kong, X., Yu, P.S. (2014). Visual Recognition by Exploiting Latent Social Links in Image Collections. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds) MultiMedia Modeling. MMM 2014. Lecture Notes in Computer Science, vol 8325. Springer, Cham. https://doi.org/10.1007/978-3-319-04114-8_11
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DOI: https://doi.org/10.1007/978-3-319-04114-8_11
Publisher Name: Springer, Cham
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