Please use this identifier to cite or link to this item:
|Title:||Story boundary detection in large broadcast news video archives - Techniques, experience and trends|
|Authors:||Chua, T.-S. |
|Keywords:||Machine learning techniques|
|Source:||Chua, T.-S.,Chang, S.-F.,Chaisorn, L.,Hsu, W. (2004). Story boundary detection in large broadcast news video archives - Techniques, experience and trends. ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia : 656-659. ScholarBank@NUS Repository.|
|Abstract:||The segmentation of news video into story units is an important step towards effective processing and management of large news video archives. In the story segmentation task in TRECVID 2003, a wide variety of techniques were employed by many research groups to segment over 120-hour of news video. The techniques employed range from simple anchor person detector to sophisticated machine learning models based on HMM and Maximum Entropy (ME) approaches. The general results indicate that the judicious use of multi-modality features coupled with rigorous machine learning models could produce effective solutions. This paper presents the algorithms and experience learned in TRECVID evaluations. It also points the way towards the development of scalable technology to process large news video corpuses.|
|Source Title:||ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 9, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.