Autotagging Facebook: Social Network Context Improves Photo Annotation
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Date
2008
Author(s)
Stone, Zak
Darrell, Trevor
Publisher
Institute of Electrical & Electronics Engineers (IEEE)
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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.
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.
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Item type:Story, Autotagging Facebook: Social Network Context Improves… : DASH Story 2012-12-09"It was a nice surprise to find this article for free, especially coming from such a respected institution. The Internet has become one of the primary ways to do research and I wish there were more open access articles and journals. My University doesn't have as many suscriptions to databases as I'd like. Thanks!"