Enhanced Semi-Supervised Learning for Automatic Video Annotation
For automatic semantic annotation of large-scale video database, the insufficiency of labeled training samples is a major obstacle. General semi-supervised learning algorithms can help solve the problem but the improvement is limited. In this paper, two semi-supervised learning algorithms, self-training and co-training, are enhanced by exploring the temporal consistency of semantic concepts in video sequences. In the enhanced algorithms, instead of individual shots, time-constraint shot clusters are taken as the basic sample units, in which most mis-classifications can be corrected before they are applied for re-training, thus more accurate statistical models can be obtained. Experiments show that enhanced self-training/co-training significantly improves the performance of video annotation