Semantic Analysis for Topical Segmentation of Videos

Topic segmentation of videos enables topic-based categorization, retrieval and browsing and also facilitates efficient video authoring. Existing video topic segmentation techniques, however, are domain specific to news or narrative videos while generic approaches based on video shot analysis generate too fine-grained micro-segments. This paper addresses this challenge through a multi-modal semantic analysis technique for recognizing topical segments.

We analyze the content of a video by using textual and audio features such as keyword synonym sets, sentence boundary information, silence/music breaks and speech similarity. Specifically, we propose a new natural language processing (NLP) technique for constructing synonym sets from video transcripts. A synonym set is a list of domainspecific keywords that are semantically related and represent a topic. We align the synonym sets with audio cues to identify the topical segments.

Our experiments with six instructional videos show that the system produced very small number of false positives, and the topical segments generated by our system are 5.5 times longer on average compared to those generated by a state-of-the-art micro-segmentation system. The system has been embedded in an e-Learning project, and the user feedback on using the generated topical segments is very encouraging. The experiments were conducted with instructional videos, but our approach is domain-general and is not restricted to instructional videos.

By: Youngja Park; Ying Li

Published in: RC24319 in 2007


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