Correlative Linear Neighborhood Propagation for Video Annotation

Recently, graph-based semi-supervised learning methods have been widely applied in multimedia research area. However, for the application of video semantic annotation in multi-label setting, these methods neglect an important characteristic of video data: The semantic concepts appear correlatively and interact naturally with each other rather than exist in isolation. In this paper, we adapt this semantic correlation into graph-based semi-supervised learning and propose a novel method named correlative linear neighborhood propagation to improve annotation performance. Experiments conducted on the Text REtrieval Conference VIDeo retrieval evaluation data set have demonstrated its effectiveness and efficiency.