%Aigaion2 BibTeX export from Idiap Publications
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         author = {Bhatt, Chidansh A. and Popescu-Belis, Andrei and Habibi, Maryam and Ingram, Sandy and Masneri, Stefano and McInnes, Fergus and Pappas, Nikolaos and Schreer, Oliver},
       projects = {Idiap, InEvent, AROLES},
          month = oct,
          title = {Multi-factor Segmentation for Topic Visualization and Recommendation: the MUST-VIS System},
      booktitle = {Proceedings of the 21st ACM International Conference on Multimedia},
           year = {2013},
          pages = {365-368},
      publisher = {ACM},
       location = {Barcelona, Spain},
           isbn = {978-1-4503-2404-5},
            url = {https://publications.idiap.ch/index.php/publications/show/2661},
            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.},
            pdf = {https://publications.idiap.ch/attachments/papers/2013/Bhatt_MM13_2013.pdf}