Broadcast News Video Story Boundary Detection in TRECVID 2005/2006
The TRECVID 2005 package contains the automatically detected story boundaries for the entire TRECVID 2005 test set (140 videos) and part of the development set (75 videos).
The detection algorithm utilizes the visual cue cluster construction (VC3) process based on the information bottleneck principle  and prosody features extracted from speech . The approach emphasizes automatic discovery of salient features and effective classification via information theory measures. The technique was shown to be effective in the TRECVID 2004 story segmentation task.
To explore unique production styles in different channels, detection is conducted in a language-dependent fashion. Different detectors are trained separately for each language - English, Chinese, and Arabic.
The 2006 test set
is different in that it includes videos captured in a time period long
after the period for the training set, or from new channels not seen in
2005. This may cause potential degradation of the performance of the story
boundary detector; however, due to the lack of annotations, we do not
have performance evaluation of the story boundary detection over the 2006
data set. To partially address this issue, we have adopted an adaptive
detection threshold so that the expected number of stories in each video
is comparable with that seen in the same channel or language over the
2005 data set. Such adaptive scheme allows for an automatic unsupervised
method for tuning the parameter of the detection method, without needing
performance validation based on annotated data.
 Winston H. Hsu and Shih-Fu Chang, "Visual Cue Cluster Construction via Information Bottleneck Principle and Kernel Density Estimation," The 4th International Conference on Image and Video Retrieval (CIVR), Singapore, July 20-22, 2005. (PDF)
 Winston H. Hsu, Lyndon
Kennedy, Shih-Fu Chang, Martin Franz, and John Smith, "Columbia-IBM
News Video Story Segmentation In TRECVID 2004," Columbia ADVENT Technical
Report 209-2005-3, New York 2005.
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