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Link prediction of multimedia social network via unsupervised face recognition

Link prediction of multimedia social network via unsupervised face recognition,10.1145/1631272.1631419,Dijun Luo,Heng Huang

Link prediction of multimedia social network via unsupervised face recognition   (Citations: 1)
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We propose a new challenge for predicting links of social networks by unsupervised face recognition on photo albums. We solve the task by formulating it into Kernel Set Discovery problem. We enhance Affinity Propagation algorithm to tackle the problem with more constraints. More specifically, the face cannot appear more than once in the same photo and we impose constraints such that detected face images in the same photograph are never clustered into the same person. We construct a synthetic dataset based on AT\&T image benchmark for empirical validation. Moreover, we validate our algorithms by a real world application which contains a real friend relation on the Web 2.0 social network system. Results indicate our Constraint Affinity Propagation method is suitable to unsupervisedly predict links of social network.
Conference: ACM Multimedia Conference - MM , pp. 805-808, 2009
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    • ...For instance semantic inference from noisy photos may become easier after eliminating noise by identifying more photos from a related social group; likewise social network analysis may be assisted by integrating images, icons, faces, and user logos in various social media or by using typical face recognition techniques [39] or by extracting visual appearance of people from videos or photos and by comparing them with appearances in other ...

    Georgios Lappas. From Web Mining to Social Multimedia Mining

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