Dahua Lin, Ashish Kapoor, Gang Hua, and Simon Baker
We present a framework for vision-assisted tagging of personal photo collections using context. Whereas previous efforts mainly focus on tagging people, we develop a unified approach to jointly tag across multiple domains (specifically people, events, and locations). The heart of our approach is a generic probabilistic model of context that couples the domains through a set of cross-domain relations. Each relation models how likely the instances in two domains are to co-occur. Based on this model, we derive an algorithm that simultaneously estimates the cross-domain relations and infers the unknown tags in a semi-supervised manner. We conducted experiments on two well-known datasets and obtained significant performance improvements in both people and location recognition. We also demonstrated the ability to infer event labels with missing timestamps (i.e. with no event features).
In Proceedings of the European Conference on Computer Vision
Publisher Springer Verlag
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