CONF Bhatt_MM'13_2013/IDIAP Multi-factor Segmentation for Topic Visualization and Recommendation: the MUST-VIS System Bhatt, Chidansh A. Popescu-Belis, Andrei Habibi, Maryam Ingram, Sandy Masneri, Stefano McInnes, Fergus Pappas, Nikolaos Schreer, Oliver Proceedings of the 21st ACM International Conference on Multimedia Barcelona, Spain 2013 ACM 365-368 978-1-4503-2404-5 https://publidiap.idiap.ch/index.php/publications/show/2661 URL 10.1145/2502081.2508120 doi 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.