weakly supervised person naming in news video
In this paper we report our experiments on assigning person names to faces as found in video frames and transcripts of the news broadcasts. We develop a face naming method that learns from labeled and unlabeled examples using iterative label propagation in a graph of connected faces or name-face pairs. The advantage of this method is that it can use very few labeled data points and incorporate the unlabeled data points during the learning process. The label propagation algorithm yields better results than a Support Vector Machine classifier trained on the same labeled data. We improve the face labeling performance by learning and using a similarity metric for comparing faces. The anchors may be problematic, since their names are typically mentioned only once, at the very beginning of the news broadcast, and they occur quite frequently. If the name-face pairs corresponding to the anchors can be separately identified, the accuracy of the overall alignments can be boosted. Hence, we develop an unsupervised model for naming anchor persons in the news.
cross-media mining image annotation