Multi-faceted contextual model for person identification in news video
Ming Zhao, Shi-Yong Neo, Hai-Kiat Goh, Tat‐Seng Chua · 2006
Person identification is very important in the domain of multimedia news as it is often the focus of events in news stories and interest of searchers. However, this detection is impeded by the imprecise audio/visual analysis tools. In this paper, we describe a multimodal and multi-faceted approach to person-x detection in news video. We make use of multimodal features extracted from text, visual and audio inherent in news video. We also incorporate multiple external sources of news from Web and parallel news archives to extract location and temporal profile of the persons. We call this second source of information the multi-faceted context. The multimodal, multi-faceted information is then fused using a rank boosting approach. Experiments on TRECVID 2003 and 2004 search queries demonstrate that our approach is effective