Separation of Professional and Amateur Video in Large Video Collections

  • Ping-Hao Wu
  • Tanaphol Thaipanich
  • Sanjay Purushotham
  • C. -C. Jay Kuo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5879)


With a rapidly dropping price in hand-held cameras and video editing software, user-generated contents are popular these days, especially on online video sharing websites. To facilitate efficient management of large video collections, it is essential to be able to separate amateur video contents from professional ones automatically. In this work, we propose several features that take into account the camera operation and the nature of amateur video clips to achieve this goal. In the proposed scheme, we estimate the number of different cameras being used in a short time interval, the shakiness of the camera, and the distance between the camera and the subject. Experimental results on a test video data set demonstrate that the camera usage can be inferred from the proposed features and reliable separation of professional and amateur video contents can be achieved.


Video classification video database 


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  1. 1.
    Brezeale, D., Cook, D.: Automatic video classification: A survey of the literature. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 38(3), 416–430 (2008)CrossRefGoogle Scholar
  2. 2.
  3. 3.
  4. 4.
    Zhu, W., Toklu, C., Liou, S.P.: Automatic news video segmentation and categorization based on closed-captioned text. In: IEEE International Conference on Multimedia and Expo. ICME 2001, August 2001, pp. 829–832 (2001)Google Scholar
  5. 5.
    Iyengar, G., Lippman, A.: Models for automatic classification of video sequences. In: Storage and Retrieval for Image and Video Databases (SPIE), pp. 216–227 (1998)Google Scholar
  6. 6.
    Jadon, R.S., Chaudhury, S., Biswas, K.K.: Generic video classification: An evolutionary learning based fuzzy theoretic approach. In: Indian Conf. Comput. Vis. Graph. Image Process, ICVGIP (2002)Google Scholar
  7. 7.
    Vasconcelos, N., Lippman, A.: Statistical models of video structure for content analysis and characterization. IEEE Transactions on Image Processing 9(1), 3–19 (2000)CrossRefGoogle Scholar
  8. 8.
    Truong, B.T., Dorai, C.: Automatic genre identification for content-based video categorization. In: Proceedings of 15th International Conference on Pattern Recognition, 2000, vol. 4, pp. 230–233 (2000)Google Scholar
  9. 9.
    Fischer, S., Lienhart, R., Effelsberg, W.: Automatic recognition of film genres. In: MULTIMEDIA 1995: Proceedings of the third ACM international conference on Multimedia, pp. 295–304. ACM, New York (1995)CrossRefGoogle Scholar
  10. 10.
    Wang, P., Cai, R., Yang, S.Q.: A hybrid approach to news video classification multimodal features, vol. 2, pp. 787–791 (December 2003)Google Scholar
  11. 11.
    Kobla, V., DeMenthon, D., Doermann, D.S.: Identifying sports videos using replay, text, and camera motion features, vol. 3972, pp. 332–343. SPIE (1999)Google Scholar
  12. 12.
    Yuan, X., Lai, W., Mei, T., Hua, X.S., Wu, X.Q., Li, S.: Automatic video genre categorization using hierarchical svm. In: 2006 IEEE International Conference on Image Processing, October 2006, pp. 2905–2908 (2006)Google Scholar
  13. 13.
    Roach, M., Mason, J., Pawlewski, M.: Video genre classification using dynamics. In: Proceedings of 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP 2001, vol. 3, pp. 1557–1560 (2001)Google Scholar
  14. 14.
    Nagasaka, A., Tanaka, Y.: Automatic video indexing and full-video search for object appearances. In: Proceedings of the IFIP TC2/WG 2.6 Second Working Conference on Visual Database Systems II, Amsterdam, The Netherlands, pp. 113–127. North-Holland Publishing Co., Amsterdam (1992)Google Scholar
  15. 15.
    Zhang, H., Wu, J., Zhong, D., Smoliar, S.W.: An integrated system for content-based video retrieval and browsing. Pattern Recognition 30(4), 643–658 (1997)CrossRefGoogle Scholar
  16. 16.
    Chalidabhongse, J., Kuo, C.C.: Fast motion vector estimation using multiresolution-spatio-temporal correlations. IEEE Transactions on Circuits and Systems for Video Technology 7(3), 477–488 (1997)CrossRefGoogle Scholar
  17. 17.
    Kim, C., Hwang, J.N.: Fast and automatic video object segmentation and tracking for content-based applications. IEEE Transactions on Circuits and Systems for Video Technology 12(2), 122–129 (2002)CrossRefGoogle Scholar
  18. 18.
    Chien, S.Y., Ma, S.Y., Chen, L.G.: Efficient moving object segmentation algorithm using background registration technique. IEEE Transactions on Circuits and Systems for Video Technology 12(7), 577–586 (2002)CrossRefGoogle Scholar
  19. 19.
    Stauffer, C., Grimson, W.E.L.: Learning patterns of activity using real-time tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 747–757 (2000)CrossRefGoogle Scholar
  20. 20.
    Morellas, V., Pavlidis, I., Tsiamyrtzis, P.: Deter: detection of events for threat evaluation and recognition. Mach. Vision Appl. 15(1), 29–45 (2003)CrossRefGoogle Scholar
  21. 21.
    Murray, D., Basu, A.: Motion tracking with an active camera. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(5), 449–459 (1994)CrossRefGoogle Scholar
  22. 22.
    Araki, S., Matsuoka, T., Takemura, H., Yokoya, N.: Real-time tracking of multiple moving objects in moving camera image sequences using robust statistics. In: International Conference on Pattern Recognition, vol. 2, p. 1433 (1998)Google Scholar
  23. 23.
    Ren, Y., Chua, C.S., Ho, Y.K.: Motion detection with nonstationary background. Mach. Vision Appl. 13(5-6), 332–343 (2003)CrossRefGoogle Scholar
  24. 24.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Vitányi, P.M.B. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23–37. Springer, Heidelberg (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ping-Hao Wu
    • 1
  • Tanaphol Thaipanich
    • 1
  • Sanjay Purushotham
    • 1
  • C. -C. Jay Kuo
    • 1
  1. 1.Ming Hsieh Department of Electrical EngineeringUniversity of Southern CaliforniaLos Angeles

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