Autotagging Facebook: Social Network Context Improves Photo Annotation

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Autotagging Facebook: Social Network Context Improves Photo Annotation

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Title: Autotagging Facebook: Social Network Context Improves Photo Annotation
Author: Zickler, Todd; Stone, Zak; Darrell, Trevor

Note: Order does not necessarily reflect citation order of authors.

Citation: Stone, Zak, Todd Zickler, and Trevor Darrell. 2008. Autotagging Facebook: Social network context improves photo annotation. Paper presented at the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Anchorage, AK. June 23-28.
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Abstract: Most personal photos that are shared online are embedded in some form of social network, and these social networks are a potent source of contextual information that can be leveraged for automatic image understanding. In this paper, we investigate the utility of social network context for the task of automatic face recognition in personal photographs. We combine face recognition scores with social context in a conditional random field (CRF) model and apply this model to label faces in photos from the popular online social network Facebook, which is now the top photo-sharing site on the Web with billions of photos in total. We demonstrate that our simple method of enhancing face recognition with social network context substantially increases recognition performance beyond that of a baseline face recognition system.
Published Version: http://dx.doi.org/10.1109/CVPRW.2008.4562956
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:2920117

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  • FAS Scholarly Articles [7456]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
 
 

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