VAST MM: multimedia browser for presentation video
Proceedings of the 6th ACM international conference on Image and video retrieval
Evaluation of video browser features and user interaction with VAST MM
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Optimal shot detection and recognition using Shiryaev-Roberts statistics
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Lecture video segmentation by automatically analyzing the synchronized slides
Proceedings of the 21st ACM international conference on Multimedia
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In multimedia-based e-Learning systems, there are strong needs for segmenting lecture videos into topic units in order to organize the videos for browsing and to provide search capability. Automatic segmentation is highly desired because of the high cost of manual segmentation. While a lot of research has been conducted on topic segmentation of transcribed spoken text, most attempts rely on domain-specific cues and formal presentation format, and require extensive training; none of these features exist in lecture videos with unscripted and spontaneous speech. In addition, lecture videos usually have few scene changes, which implies that the visual information that most video segmentation methods rely on is not available. Furthermore, even when there are scene changes, they do not match with the topic transitions. In this paper, we make use of the transcribed speech text extracted from the audio track of video to segment lecture videos into topics. We review related research and propose a new segmentation approach. Our approach utilizes features such as noun phrases and combines multiple content-based and discourse-based features. Our preliminary results show that the noun phrases are salient features and the combination of multiple features is promising to improve segmentation accuracy.