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 [BibTeX] [Marc21]
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.
Projects Idiap
Authors Bhatt, Chidansh A.
Popescu-Belis, Andrei
Habibi, Maryam
Ingram, Sandy
Masneri, Stefano
McInnes, Fergus
Pappas, Nikolaos
Schreer, Oliver
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Total mark: 0
  • Bhatt_MM13_2013.pdf