High level segmentation of instructional videos based on content density

Dinh Q. Phung, Svetha Venkatesh, Chitra Dorai

Research output: Contribution to conferencePaperpeer-review

17 Citations (Scopus)


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.

Original languageEnglish
Number of pages4
Publication statusPublished - 1 Dec 2002
Externally publishedYes
Event10th International Conference of Multimedia - Juan les Pins, France
Duration: 1 Dec 20026 Dec 2002


Conference10th International Conference of Multimedia
CityJuan les Pins

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