关键词: 视频标注, 多语义标注, 时序概率超图, 时间相关性, 半监督学习

Abstract: For bridging semantic gap between low-level features and user’s information needs in the semantic-based video retrieval system, this paper proposes a temporal-probabilistic hypergraph model which fuses temporal factor in the process of construction, and then introduces a video multi-semantic annotation framework based on temporal-probabilistic hypergraph model, which is named TPH-VMLAF. The TPH-VMLAF combines the temporal consistency property of video data, and annotates video segments with semantic keywords via temporal-probabilistic hypergraph based multi-label semi-supervised learning approach. The proposed framework addresses two important issues simultaneously in the process of video annotation, the insufficiency of labeled videos and the multiple labeling issues. Experimental results indicate that this method improves the precision of annotation and shows good performance.

Key words: video annotation, multi-semantic annotation, temporal-probabilistic hypergraph, temporal consistency, semi-supervised learning