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A Support Vector Machine Approach for Video Shot Detection

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New Directions in Intelligent Interactive Multimedia

Abstract

The first step towards indexing and content based video retrieval is video shot detection. Existing methodologies for video shot detection are mostly threshold dependent. No prior knowledge about the video content makes such methods sensitive to video content. To ameliorate this shortcoming we propose a learning based methodology using a set of features that are specifically designed to capture the differences among hard cuts, gradual transitions and normal sequences of frames simultaneously. A Support Vector Machine (SVM) classifier is trained both to locate shot boundaries and characterize transition types. Numerical experiments using a variety of videos demonstrate that our method is capable of accurately detecting and discriminating shot transitions in videos with different characteristics.

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References

  1. Bescós, J., Cisneros, G., Martínez, J.M., Menéndez, J.M., Cabrera, J.: A Unified Model for Techniques on Video-Shot Transition Detection. IEEE Trans. Multimedia 7(2), 293–307 (2005)

    Article  Google Scholar 

  2. Bimbo, A.D.: Visual Information Retrieval. Morgan Kaufmann Publishers, Inc., San Francisco (1999)

    Google Scholar 

  3. Boccignone, G., Chianese, A., Moscato, V., Picariello, A.: Foveated Shot Detection for Video Segmentation. IEEE Trans. Circuits and Systems for Video Technology 15(3), 365–377 (2005)

    Article  Google Scholar 

  4. Boreczky, J.S., Rowe, L.A.: Comparison of Video Shot Boundary Detection Techniques. In: Proc. SPIE Storage and Retrieval for Image and Video Databases, vol. 2664, pp. 170–179 (1996)

    Google Scholar 

  5. Cernekova, Z., Pitas, I., Nikou, C.: Information Theory-Based Shot Cut/Fade Detection and Video Summarization. IEEE Trans. Circuits and Systems for Video Technology 16(1), 82–91 (2006)

    Article  Google Scholar 

  6. Cortes, C., Vapnik, V.: Support-vector network. Machine Learning 20(3), 273–297 (1995)

    MATH  Google Scholar 

  7. Dalatsi, C., Krinidis, S., Tsekeridou, S., Pitas, I.: Use of Support Vector Machines based on Color and Motion Features for Shot Boundary Detection. In: International Symposium on Telecommunications (2001)

    Google Scholar 

  8. Feng, H., Fang, W., Liu, S., Fang, Y.: A new general framework for shot boundary detection and key-frame extraction. In: Proc. 7th ACM SIGMM Int. Workshop Multimedia Inf. Retrieval, pp. 121–126 (2005)

    Google Scholar 

  9. Fernando, W.A.C., Canagarajah, C.N., Bull, D.R.: Fade and dissolve detection in uncompressed and compressed video sequences. In: Proc. IEEE Int. Conf. Image Processing, vol. 3, pp. 299–303 (1999)

    Google Scholar 

  10. Gargi, U., Kasturi, R., Strayer, S.H.: Performance characterization of video-shot-detection methods. IEEE Trans. Circuits and Systems for Video Technology 10(1), 1–13 (2000)

    Article  Google Scholar 

  11. Hanjalic, A.: Shot-boundary detection: Unraveled and resolved? IEEE Trans. Circuits and Systems for Video Technology 12(2), 90–105 (2002)

    Article  Google Scholar 

  12. Kasturi, R., Lain, R.: Dynamic Vision. In: Kasturi, R., Lain, R. (eds.) Computer Vision: Principles, pp. 469–480. IEEE Computer Society Press, Washington (1991)

    Google Scholar 

  13. Knerr, S., Personnaz, L., Dreyfus, G.: Single-layer learning revisited: a stepwise procedure for building and training a neural network. In: Fogelman, J. (ed.) Neurocomputing Algorithms, Architectures and Applications. Springer, Heidelberg (1990)

    Google Scholar 

  14. Lelescu, D., Schonfeld, D.: Statistical sequential analysis for real-time video scene change detection on compressed multimedia bitstream. IEEE Trans. Multimedia 5(1), 106–117 (2003)

    Article  Google Scholar 

  15. Lienhart, R.: Comparison of automatic shot boundary detection algorithms. In: Proc. SPIE Storage and Retrieval for Image and Video Databases VII, San Jose, CA, vol. 3656, pp. 290–301 (1999)

    Google Scholar 

  16. Lienhart, R.: Reliable dissolve detection. In: Proc. SPIE Storage and Retrieval for Media Databases 2001, vol. 4315, pp. 219–230 (2001)

    Google Scholar 

  17. Nagasaka, A., Tanaka, Y.: Automatic video indexing and full-video search for object appearances. In: Knuth, E., Wegner, L.M. (eds.) Visual Database Systems II, pp. 113–127. Elsevier, Amsterdam (1995)

    Google Scholar 

  18. Ngo, C.W., Pong, T.C., Chin, R.T.: Video partitioning by temporal slice coherence. IEEE Trans. Circuits and Systems for Video Technology 11(8), 941–953 (2001)

    Article  Google Scholar 

  19. NIST, Homepage of Trecvid Evaluation. [Online]. http://www-nlpir.nist.gov/projects/trecvid/

  20. Yuan, J., Wang, H., Xiao, L., Zheng, W., Li, J., Lin, F., Zhang, B.: A Formal Study of Shot Boundary Detection. IEEE Trans. Circuits and Systems for Video Technology 17(2), 168–186 (2007)

    Article  Google Scholar 

  21. Zabih, R., Miller, J., Mai, K.: Feature-Based Algorithms for Detecting and Classifying Production Effects. Multimedia Systems 7(2), 119–128 (1999)

    Article  Google Scholar 

  22. Zhang, H.J., Kankanhalli, A., Smoliar, S.W.: Automatic partitioning of full-motion video. Multimedia Systems 1(1), 10–28 (1993)

    Article  Google Scholar 

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George A. Tsihrintzis Maria Virvou Robert J. Howlett Lakhmi C. Jain

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Chasanis, V., Likas, A., Galatsanos, N. (2008). A Support Vector Machine Approach for Video Shot Detection. In: Tsihrintzis, G.A., Virvou, M., Howlett, R.J., Jain, L.C. (eds) New Directions in Intelligent Interactive Multimedia. Studies in Computational Intelligence, vol 142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68127-4_5

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  • DOI: https://doi.org/10.1007/978-3-540-68127-4_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-68126-7

  • Online ISBN: 978-3-540-68127-4

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