Community Discovery from Movie and Its Application to Poster Generation

  • Yan Wang
  • Tao Mei
  • Xian-Sheng Hua
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6523)


Discovering roles and their relationship is critical in movie content analysis. However, most conventional approaches ignore the correlations among roles or require rich metadata such as casts and scripts, which makes them not practical when little metadata is available, especially in the scenarios of IPTV and VOD systems. To solve this problem, we propose a new method to discover key roles and their relationship by treating a movie as a small community. We first segment a movie into a hierarchical structure (including scene, shot, and key-frame), and perform face detection and grouping on the detected key-frames. Based on such information, we then create a community by exploiting the key roles and their correlations in this movie. The discovered community provides a wide variety of applications. In particular, we present in this paper the automatic generation of video poster (with four different visualizations) based on the community, as well as preliminary experimental results.


Content-based movie analysis social network video poster 


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  1. 1.
    Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.J.: Face recognition: A literature survey. ACM Computing Surveys 35(47), 399–458 (2003)CrossRefGoogle Scholar
  2. 2.
    Satoh, S., Kanade, T.: Name-It: Association of face and name in video. In: Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, pp. 368–373 (1997)Google Scholar
  3. 3.
    Everingham, M., Sivic, J., Zisserman, A.: Hello! My name is Buffy—automatic naming of characters in TV video. In: Proceedings of the British Machine Vision Conference (2006)Google Scholar
  4. 4.
    Liu, C., Jiang, S., Huang, Q.: Naming faces in broadcast news video by image google. In: Proceeding of ACM International Conference on Multimedia, Vancouver, Canada, pp. 717–720 (2008)Google Scholar
  5. 5.
    Zhang, Y.F., Xu, C., Lu, H., Huang, Y.M.: Character identification in feature-length films using global face-name matching. Trans. on Multimedia 11(7), 1276–1288 (2009)CrossRefGoogle Scholar
  6. 6.
    Weng, C.Y., Chu, W.T., Wu, J.L.: RoleNet: treat a movie as a small society. In: Proceedings of the international Workshop on Multimedia Information Retrieval, Augsburg, Bavaria, Germany, pp. 51–60 (2007)Google Scholar
  7. 7.
    AT&T: U-verse tv,
  8. 8.
    Mei, T., Hua, X.S., Zhu, C.Z., Zhou, H.Q., Li, S.: Home video visual quality assessment with spatiotemporal factors. IEEE Trans. on Circuits and Systems for Video Techn. 17(6), 699–706 (2007)CrossRefGoogle Scholar
  9. 9.
    Cao, Z., Yin, Q., Tang, X., Sun, J.: Face recognition with learning-based descriptor. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (2010)Google Scholar
  10. 10.
    Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315, 972–976 (2007)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Scott, J.P.: Social Network Analysis: A Handbook. SAGE Publications (2000)Google Scholar
  12. 12.
    Frascara, J.: Communication design: principles, methods, and practice. Allworth Communications, Inc. (2004)Google Scholar
  13. 13.
    Mei, T., Yang, B., Yang, S.Q., Hua, X.S.: Video collage: Presenting a video sequence using a single image. The Visual Computer 25(1), 39–51 (2009)CrossRefGoogle Scholar
  14. 14.
    Wang, Y., Mei, T., Wang, J., Hua, X.S.: Dynamic video collage. In: International Conference on MultiMedia Modeling, Chongqing, China, pp. 793–795 (2010)Google Scholar
  15. 15.
    Wang, J., Sun, J., Quan, L., Tang, X., Shum, H.Y.: Picture collage. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 347–354 (2006)Google Scholar
  16. 16.
    Skolos, N., Wedell, T.: Type, Image, Message: A Graphic Design Layout Workshop. Rockport Publishers (2006)Google Scholar
  17. 17.
    Liu, T., Sun, J., Zheng, N.N., Tang, X., Shum, H.Y.: Learning to detect a salient object. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yan Wang
    • 1
    • 2
  • Tao Mei
    • 1
  • Xian-Sheng Hua
    • 1
  1. 1.Microsoft Research AsiaBeijingP.R. China
  2. 2.University of Science and Technology of ChinaHefeiP.R. China

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