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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Vinciarelli, A., Fernà ndez, F., Favre, S.: Semantic segmentation of radio programs using social network analysis and duration distribution modeling. pp. 779 – 782. IEEE (2007)
Park, S.B., Oh, K.J., Jo, G.S.: Social network analysis in a movie using character-net. Kluwer Academic Publishers (2012)
Weng, C.Y., Chu, W.T., Wu, J.L.: RoleNet: movie analysis from the perspective of social networks. IEEE Trans. Multimed. 11(2), 256–271 (2009)
Yeh, M.C., Tseng, M.C., Wu, W.P.: Automatic social network construction from movies using film-editing cues, vol. 131, no. 5, pp. 242–247 (2012)
Tran, Q.D., Jung, J.E.: CoCharNet: extracting social networks using character co-occurrence in movies. J. Univ. Comput. 21(6), 796–815 (2015)
Tran, Q.D., Hwang, D., Lee, O.J., et al.: Exploiting character networks for movie summarization. Multimed. Tools Appl. 1–13 (2016)
Tsai, C.M., Kang, L.W., Lin, C.W., et al.: Scene-based movie summarization via role - community networks. IEEE Trans. Circuits Syst. Video Technol. 23(11), 1927–1940 (2013)
Liu, X., Kan, M., Wu, W., et al.: VIPLFaceNet: an open source deep face recognition SDK. Front. Comput. Sci. 11(2), 208–218 (2017)
Rasheed, Z., Shah, M.: Detection and representation of scenes in videos. IEEE Trans. Multimed. 7(6), 1097–1105 (2005)
Li, Y., Yang, X., Luo, J.: Semantic video entity linking based on visual content and metadata. In: IEEE International Conference on Computer Vision. IEEE Computer Society, Article in a Conference Proceedings
Tran, Q.D., Hwang, D., Lee, O.J., Jung, J.J.: A novel method for extracting dynamic character network from movie. In: Jung, J., Kim, P. (eds.) BDTA 2016. LNICST, vol. 194, pp. 48–53. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58967-1_6
Sang, J., Xu, C.: Character-based movie summarization. In: ACM International Conference on Multimedia. pp. 855–858. ACM (2010)
Li, J.Y., Kang, L.W., Tsai, C.M., et al.: Learning-based movie summarization via role-community analysis and feature fusion. In: IEEE, International Workshop on Multimedia Signal Processing, pp. 1–6. IEEE (2015)
Xuan, H.P., Jung, J.J., Le, A.V., et al.: Exploiting social contexts for movie recommendation. Malays. J. Comput. Sci. 27(1), 68–79 (2014)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-73600-6_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73599-3
Online ISBN: 978-3-319-73600-6
eBook Packages: Computer ScienceComputer Science (R0)