ERP/MMR Algorithm for Classifying Topic‐Relevant and Topic‐Irrelevant Visual Shots of Documentary Videos

We propose and evaluate a video summarization method based on a topic relevance model, a maximal marginal relevance (MMR), and discriminant analysis to generate a semantically meaningful video skim. The topic relevance model uses event‐related potential (ERP) components to describe the process of to...

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Bibliographic Details
Published in:Journal of the American Society for Information Science and Technology 2019-09, Vol.70 (9), p.931-941
Main Authors: Kim, Hyun Hee, Kim, Yong Ho
Format: Article
Language:English
Subjects:
Algorithms
Discriminant analysis
Event-related potentials
Ground truth
Judgment
Summarization
Topics
Truth
Video data
Video Technology
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Summary:We propose and evaluate a video summarization method based on a topic relevance model, a maximal marginal relevance (MMR), and discriminant analysis to generate a semantically meaningful video skim. The topic relevance model uses event‐related potential (ERP) components to describe the process of topic relevance judgment. More specifically, the topic relevance model indicates that N400 and P600, which have been successfully applied to the mismatch process of a stimulus and the discourse‐internal reorganization and integration process of a stimulus, respectively, are used for the topic mismatch process of a topic‐irrelevant video shot and the topic formation process of a topic‐relevant video shot. To evaluate our proposed ERP/MMR‐based method, we compared the video skims generated by the ERP/MMR‐based, ERP‐based, and shot boundary detection (SBD) methods with ground truth skims. The results showed that at a significance level of 0.05, the ROUGE‐1 scores of the ERP/MMR method are statistically higher than those of the SBD method, and the diversity scores of the ERP/MMR method are statistically higher than those of the ERP method. This study suggested that the proposed method may be applied to the construction of a video skim without operational intervention, such as the insertion of a black screen between video shots.
ISSN:2330-1635
2330-1643
DOI:10.1002/asi.24179