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Link prediction of multimedia social network via unsupervised face recognition
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International Multimedia Conference archive
Proceedings of the seventeen ACM international conference on Multimedia table of contents
Beijing, China
SESSION: Short papers session 3: applications and systems table of contents
Pages 805-808  
Year of Publication: 2009
ISBN:978-1-60558-608-3
Authors
Dijun Luo  The University of Texas at Arlington, Arlington, TX, USA
Heng Huang  The University of Texas at Arlington, Arlington, TX, USA
Sponsor
SIGMULTIMEDIA: ACM Special Interest Group on Multimedia
Publisher
ACM  New York, NY, USA
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ABSTRACT

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.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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