Hierarchical topical segmentation in instructional films based on cinematic expressive functions
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Synchronization of lecture videos and electronic slides by video text analysis
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Topic transition detection using hierarchical hidden Markov and semi-Markov models
Proceedings of the 13th annual ACM international conference on Multimedia
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Automatically partitioning instructional videos into topic sections is a challenging problem in e-learning environments for efficient content management and cataloging. This paper addresses this problem by proposing a novel density function to delineate sections underscored by changes in topics in instructional and training videos. The content density function draws guidance from the observation that topic boundaries coincide with the ebb and flow of the 'density' of content shown in these videos. Based on this function, we propose two methods for high-level segmentation by determining topic boundaries. We study the performance of the two methods on eight training videos, and our experimental results demonstrate the effectiveness and robustness of the two proposed high-level segmentation algorithms for learning media.