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ATLAS: Automatic Temporal Segmentation and Annotation of Lecture Videos Based on Modelling Transition Time

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Published:03 November 2014Publication History

ABSTRACT

The number of lecture videos available is increasing rapidly, though there is still insufficient accessibility and traceability of lecture video contents. Specifically, it is very desirable to enable people to navigate and access specific slides or topics within lecture videos. To this end, this paper presents the ATLAS system for the VideoLectures.NET challenge (MediaMixer, transLectures) to automatically perform the temporal segmentation and annotation of lecture videos. ATLAS has two main novelties: (i) a SVMhmm model is proposed to learn temporal transition cues and (ii) a fusion scheme is suggested to combine transition cues extracted from heterogeneous information of lecture videos. According to our initial experiments on videos provided by VideoLectures.NET, the proposed algorithm is able to segment and annotate knowledge structures based on fusing temporal transition cues and the evaluation results are very encouraging, which confirms the effectiveness of our ATLAS system.

References

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  1. ATLAS: Automatic Temporal Segmentation and Annotation of Lecture Videos Based on Modelling Transition Time

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        • Published in

          cover image ACM Conferences
          MM '14: Proceedings of the 22nd ACM international conference on Multimedia
          November 2014
          1310 pages
          ISBN:9781450330633
          DOI:10.1145/2647868

          Copyright © 2014 ACM

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          New York, NY, United States

          Publication History

          • Published: 3 November 2014

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          MM '14 Paper Acceptance Rate55of286submissions,19%Overall Acceptance Rate995of4,171submissions,24%

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