Multi-factor segmentation for topic visualization and recommendation: the MUST-VIS system

Authors:
Chidansh Amitkumar Bhatt;Andrei Popescu-Belis;Maryam Habibi;Sandy Ingram;Stefano Masneri;Fergus McInnes;Nikolaos Pappas;Oliver Schreer
Affiliations:
Idiap Research Institute, Martigny, Switzerland;Idiap Research Institute, Martigny, Switzerland;Idiap and EPFL, Martigny, Switzerland;Klewel SA, Martigny, Switzerland;Heinrich Hertz Institute, Berlin, Germany;University of Edinburgh, Edinburgh, United Kingdom;Idiap and EPFL, Martigny, Switzerland;Heinrich Hertz Institute, Berlin, Germany
Venue:
Proceedings of the 21st ACM international conference on Multimedia
Year:
2013

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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.