Abstract
Video character relationship mining, as a kind of video semantic analysis, has become a hot topic. Based on the temporal and spatial context and video semantic information of the scene, this paper proposes a method of exploiting character co-occurrence relationship, and constructs character’s social network called SRN through the quantitative relationship among the characters. Based on SRN network, we can get rid of the limitation of the traditional feature-based approach and carry out more in-depth video semantic analysis. By analyzing the characters in the SRN network, a community identification method based on the core characters is proposed, which can automatically confirm the core characters in the video and dig out the communities around the core characters. In this paper, lots of movie videos are used to experiment, and experimental results show the effectiveness of SRN model and community identification method.
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Acknowledgment
This work is supported by National Science Foundation of China (No. 61571453) and Natural Science Foundation of Hunan, China (No. 14JJ3010). The authors are grateful for the anonymous reviewers who made constructive comments.
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He, J., Xie, Y., Luan, X., Zhang, L., Zhang, X. (2018). SRN: The Movie Character Relationship Analysis via Social Network. In: Schoeffmann, K., et al. MultiMedia Modeling. MMM 2018. Lecture Notes in Computer Science(), vol 10705. Springer, Cham. https://doi.org/10.1007/978-3-319-73600-6_25
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DOI: https://doi.org/10.1007/978-3-319-73600-6_25
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