A multi-modal two-level framework for news story segmentation was proposed in Chaisorn et al. (2002). This paper presents our extended work scaled to cope with a large news video corpus used in TRECVID 2003 evaluation. We divided our system into two levels: the shot level that classifies input video shots into one of the predefined categories using a hybrid of heuristic and learning based approaches; and story level that performs story segmentation using the HMM framework based on the output of shot level and other temporal features. A heuristic rule-based technique is then employed to classify each detected story into "news" or "miscellaneous". We evaluated our system on over 120 hours of news video and showed that our system could achieve an accuracy of more than 77%. Our system came first in the TRECVID 2003 story segmentation task
['Lekha Chaisorn', 'Tat-Seng Chua', 'Chin-Hui Lee', 'Qi Tian']
A hierarchical approach to story segmentation of large broadcast news video corpus