Story Detection in News Videos

A particular interest exists in detecting stories in news videos. The results of the news story segmentation can be further applied in tasks, such as video summarization, indexing and retrieval. In this work, I have developed a new framework for segmenting the news programs into different story topics. The proposed method is constructed based on the Shot Connectivity Graph and utilizes both visual and textual contents of the video. With a series of anchor detection, weather and sporting news localization and story merging processes, the input news videos is finally segmented into stories, each of which consists coherent semantic contents.  This work has achieved very high accuracy in the TRECVID evaluation competition 2004, and UCF vision team was invited to given an oral presentation in the forum.