TY - GEN
T1 - Threading stories and generating topic structures in news videos across different sources
AU - Wu, Xiao
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2005
Y1 - 2005
N2 - News videos delivered from different sources constitute a huge volume of daily information. These videos, overall, form a huge collection of news stories that are intertwined with various novel and old topic themes. To date, it remains a challenging task on how to automatically extract a concise view of news stories according to topic themes. This doctoral thesis studies the issues in story dependency threading and topical auto-documentary in news stories. Initially, a co-clustering algorithm is proposed to perform the news story clustering by exploiting the duality between stories and multi-modal concepts. Then, the novelty and redundancy detection is performed to capture the relationship among stories of a topic. To facilitate the fast navigation of news topic, a novel topic structure is then proposed to chains the dependencies of stories. A main thread is extracted to highlight the important aspects of a theme. A news video editing optimization algorithm can be directly applied to automatically select suitable video and speech contents from the original video source to create an edited video documentary. Copyright © 2005 ACM.
AB - News videos delivered from different sources constitute a huge volume of daily information. These videos, overall, form a huge collection of news stories that are intertwined with various novel and old topic themes. To date, it remains a challenging task on how to automatically extract a concise view of news stories according to topic themes. This doctoral thesis studies the issues in story dependency threading and topical auto-documentary in news stories. Initially, a co-clustering algorithm is proposed to perform the news story clustering by exploiting the duality between stories and multi-modal concepts. Then, the novelty and redundancy detection is performed to capture the relationship among stories of a topic. To facilitate the fast navigation of news topic, a novel topic structure is then proposed to chains the dependencies of stories. A main thread is extracted to highlight the important aspects of a theme. A news video editing optimization algorithm can be directly applied to automatically select suitable video and speech contents from the original video source to create an edited video documentary. Copyright © 2005 ACM.
KW - Novelty/redundancy detection
KW - Topic structure
KW - Topic threading
UR - https://www.scopus.com/pages/publications/77951920473
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-77951920473&origin=recordpage
U2 - 10.1145/1101149.1101369
DO - 10.1145/1101149.1101369
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 1595930442
SN - 9781595930446
T3 - Proceedings of the 13th ACM International Conference on Multimedia, MM 2005
SP - 1047
EP - 1048
BT - Proceedings of the 13th ACM International Conference on Multimedia, MM 2005
T2 - 13th ACM International Conference on Multimedia, MM 2005
Y2 - 6 November 2005 through 11 November 2005
ER -