Multi-factor Segmentation for Topic Visualization and Recommendation: the MUST-VIS System
| Type of publication: | Conference paper |
| Citation: | Bhatt_MM'13_2013 |
| Publication status: | Published |
| Booktitle: | Proceedings of the 21st ACM International Conference on Multimedia |
| Year: | 2013 |
| Month: | October |
| Pages: | 365-368 |
| Publisher: | ACM |
| Location: | Barcelona, Spain |
| ISBN: | 978-1-4503-2404-5 |
| URL: | https://publications.idiap.ch/ind... |
| DOI: | 10.1145/2502081.2508120 |
| Abstract: | This paper presents the MUST-VIS system for the MediaMixer/VideoLectures.NET Temporal Segmentation and Annotation Grand Challenge. The system allows users to visualize a lecture as a series of segments represented by keyword clouds, with relations to other similar lectures and segments. Segmentation is performed using a multi-factor algorithm which takes advantage of the audio (through automatic speech recognition and word-based segmentation) and video (through the detection of actions such as writing on the blackboard). The similarity across segments and lectures is computed using a content-based recommendation algorithm. Overall, the graph-based representation of segment similarity appears to be a promising and cost-effective approach to navigating lecture databases. |
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| Projects |
Idiap InEvent AROLES |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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