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Predicting Violence Rating Based on Pairwise Comparison

Ying JI, Yu WANG, Jien KATO, Kensaku MORI

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Summary :

With the rapid development of multimedia, violent video can be easily accessed in games, movies, websites, and so on. Identifying violent videos and rating violence extent is of great importance to media filtering and children protection. Many previous studies only address the problems of violence scene detection and violent action recognition, yet violence rating problem is still not solved. In this paper, we present a novel video-level rating prediction method to estimate violence extent automatically. It has two main characteristics: (1) a two-stream network is fine-tuned to construct effective representations of violent videos; (2) a violence rating prediction machine is designed to learn the strength relationship among different videos. Furthermore, we present a novel violent video dataset with a total of 1,930 human-involved violent videos designed for violence rating analysis. Each video is annotated with 6 fine-grained objective attributes, which are considered to be closely related to violence extent. The ground-truth of violence rating is given by pairwise comparison method. The dataset is evaluated in both stability and convergence. Experiment results on this dataset demonstrate the effectiveness of our method compared with the state-of-art classification methods.

Publication
IEICE TRANSACTIONS on Information Vol.E103-D No.12 pp.2578-2589
Publication Date
2020/12/01
Publicized
2020/08/28
Online ISSN
1745-1361
DOI
10.1587/transinf.2020EDP7056
Type of Manuscript
PAPER
Category
Data Engineering, Web Information Systems

Authors

Ying JI
  Nagoya University
Yu WANG
  Ritsumeikan University
Jien KATO
  Ritsumeikan University
Kensaku MORI
  Nagoya University

Keyword