Segmentation of video by clustering and graph analysis
Computer Vision and Image Understanding
The LIMSI Broadcast News transcription system
Speech Communication - Special issue on automatic transcription of broadcast news data
Motion-Based Video Representation for Scene Change Detection
International Journal of Computer Vision
Video Scene Segmentation via Continuous Video Coherence
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Robust Real-Time Face Detection
International Journal of Computer Vision
Scene Determination Based on Video and Audio Features
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
Automated high-level movie segmentation for advanced video-retrieval systems
IEEE Transactions on Circuits and Systems for Video Technology
Tracking news stories across different sources
Proceedings of the 13th annual ACM international conference on Multimedia
Movie scene segmentation using background information
Pattern Recognition
Vlogging: A survey of videoblogging technology on the web
ACM Computing Surveys (CSUR)
Cross-lingual retrieval of identical news events by near-duplicate video segment detection
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
On-line video abstract generation of multimedia news
Multimedia Tools and Applications
Expert Systems with Applications: An International Journal
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In this paper, we present a framework for segmenting the news programs into different story topics. The proposed method utilizes both visual and text information of the video. We represent the news video by a Shot Connectivity Graph (SCG), where the nodes in the graph represent the shots in the video, and the edges between nodes represent the transitions between shots. The cycles in the graph correspond to the story segments in the news program. We first detect the cycles in the graph by finding the anchor persons in the video. This provides us with the coarse segmentation of the news video. The initial segmentation is later refined by the detections of the weather and sporting news, and the merging of similar stories. For the weather detection, the global color information of the images and the motion of the shots are considered. We have used the text obtained from automatic speech recognition (ASR) for detecting the potential sporting shots to form the sport stories. Adjacent stories with similar semantic meanings are further merged based on the visual and text similarities. The proposed framework has been tested on a widely used data set provided by NIST, which contains the ground truth of the story boundaries, and competitive evaluation results have been obtained.