Semantic Topic Discovery for Lecture Video
- Publication Type:
- Conference Proceeding
- Springer series "Advances in Intelligent Systems and Computing", 2019
- Issue Date:
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With more and more lecture videos are available on the In- ternet, on-line learning and e-learning are getting increasing concerns because of many advantages such as high degree of interactivity. The se- mantic content discovery for lecture video is a key problem. In this paper, we propose a Multi-modal LDA model, which discovers the semantic top- ics of lecture videos by considering audio and visual information. Specif- ically, the speaking content and the information of presentation slides are extracted from the lecture videos. With the proposed inference and learning algorithm, the semantic topics of the video can be discovered. The experimental results show that the proposed method can e ectively discover the meaningful semantic characters of the lecture videos.
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