Tag-based social image search with visual-text joint hypergraph learning
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Tag suggestion and localization for web videos by bipartite graph matching
WSM '11 Proceedings of the 3rd ACM SIGMM international workshop on Social media
Assistive tagging: A survey of multimedia tagging with human-computer joint exploration
ACM Computing Surveys (CSUR)
Automatic image tagging using two-layered Bayesian networks and mobile data from smart phones
Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia
Tagging photos using users' vocabularies
Neurocomputing
When Amazon Meets Google: Product Visualization by Exploring Multiple Web Sources
ACM Transactions on Internet Technology (TOIT)
A mobile picture tagging system using tree-structured layered Bayesian networks
Mobile Information Systems
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As one of the emerging Web 2.0 activities, tagging becomes a popular approach to manage personal media data, such as photo albums. A dilemma in tagging behavior is the users' manual efforts and the tagging accuracy: exhaustively tagging all photos in an album is labor-intensive and time-consuming, and simply entering tags for the whole album leads to unsatisfying results. In this paper, we propose a semi-automatic tagging scheme that aims to facilitate users in photo album tagging. The scheme is able to achieve a good trade-off between manual efforts and tagging accuracy as well as to adjust tagging performance according to the user's customization. For a given album, it first selects a set of representative exemplars for manual tagging via a temporally consistent affinity propagation algorithm, and the tags of the rest of the photos are automatically inferred. Then a constrained affinity propagation algorithm is applied to select a new set of exemplars for manual tagging in an incremental manner, based on which the performance of the tag inference in the previous round can be estimated. If the results are not satisfying enough, a further round of exemplar selection and tag inference will be implemented. This process repeats until satisfactory tagging results are achieved, and users can also stop the process at any time. Experimental results on real-world Flickr photo albums have demonstrated the effectiveness and usefulness of the proposed scheme.