Detecting Significant Events in Lecture Video using Supervised Machine Learning

被引：2

作者：

Brooks, Christopher ^{[1
]}

Amundson, Kristofor ^{[1
]}

Greer, Jim ^{[1
]}

机构：

[1] Univ Saskatchewan, Dept Comp Sci, Lab Adv Res Intelligent Educ Syst ARIES, Saskatoon, SK, Canada

来源：

ARTIFICIAL INTELLIGENCE IN EDUCATION: BUILDING LEARNING SYSTEMS THAT CARE: FROM KNOWLEDGE REPRESENTATION TO AFFECTIVE MODELLING | 2009年 / 200卷

关键词：

Course casting; video capture; supervised machine learning; significant events; chaptering; indexing; multimedia; HIGH AGREEMENT; LOW KAPPA;

D O I：

10.3233/978-1-60750-028-5-483

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper describes work we are doing to identify significant events in video captures of academic lectures. Unlike other approaches which tend to define per-image comparison threshold values based on intuition or empirically derived results, we use supervised machine teaming techniques to automatically determine appropriate image characteristics based on end-users understanding of what constitutes an important event. This makes our approach more adaptable to different kinds of content, and still provides a substantial level of agreement with human experts.

引用

页码：483 / +

页数：2

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