Semi-Automatic Tagging of Photo Albums via Exemplar Selection and Tag Inference

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.