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Automatic Shot-Change Detection Algorithm Based on Visual Rhythm Extraction

  • Kwang-Deok Seo
  • Seong Jun Park
  • Jin-Soo Kim
  • Samuel Moon-Ho Song
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4141)

Abstract

To store and retrieve large-scale video data sets effectively, the process of shot-change detection is an essential step. In this paper, we propose an automatic shot-change detection algorithm based on Visual Rhythm Spectrum. The Visual Rhythm Spectrum contains distinctive patterns or visual features for many different types of video effects. For the improvement of detection speed, the proposed algorithm is executed by using the partial data of digital compressed video. The proposed detection algorithm can be universally applied to various kinds of shot-change categories such as scene-cuts and wipes. It is shown by simulations that the proposed detection algorithm outperforms other existing approaches.

Keywords

Discrete Cosine Transform Oblique Line Binary Mask Inverse Discrete Cosine Transform Video Effect 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Chen, S., Shyu, M., Liao, W., Zhang, C.: Scene Change Detection by Audio and Video Clues. In: Proc. of IEEE International Conference on Multimedia and Expo., pp. 365–368 (August 2002)Google Scholar
  2. 2.
    Kim, J., Suh, S., Sull, S.: Fast Scene Change Detection for Personal Video Recorder. IEEE Trans. on Consumer Electronics 49(3), 683–688 (2003)CrossRefGoogle Scholar
  3. 3.
    Lelescu, D., Schonfeld, D.: Statistical Sequential Analysis for Real-time Video Scene Change Detection on Compressed Multimedia Bitstream. IEEE Trans. on Multimedia 5(1), 106–117 (2003)CrossRefGoogle Scholar
  4. 4.
    Zabih, R., Miller, J., Mai, K.: A Feature-based Algorithm for Detection and Classifying Scene Breaks. ACM Multimedia, 189–200 (1995)Google Scholar
  5. 5.
    Yeo, B., Liu, B.: Rapid Scene Analysis on Compressed Video. IEEE Trans. on Circuits and Systems for Video Technology 5(6), 533–544 (1995)CrossRefGoogle Scholar
  6. 6.
    Meng, J., Juan, Y., Chang, S.: Scene Change Detection in a MPEG Compressed Video Sequence. In: Proc. of SPIE Symposium on Digital Video Compression, pp. 14–25 (1995)Google Scholar
  7. 7.
    Calic, J., Izuierdo, E.: Efficient Key-frame Extraction and Video Analysis. In: Proc. of International Conference on Information Technology: Coding and Computing, pp. 28–33 (April 2002)Google Scholar
  8. 8.
    Yeo, B., Liu, B.: On the Extraction of DC Sequence from MPEG Compressed Video. In: Proc. of International Conference on Image Processing, vol. 2, pp. 260–263 (October 1995)Google Scholar
  9. 9.
    Song, J., Yeo, B.: Spatially Reduced Image Extraction from MPEG-2 Video: Fast Algorithms and Applications. In: Proc. of SPIE Storage and Retrieval for Image and Video Database VI, vol. 3312, pp. 93–107 (1998)Google Scholar
  10. 10.
    Salton, G.: Automatic Text Processing; the Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley Publishing Company, Reading (1989)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kwang-Deok Seo
    • 1
  • Seong Jun Park
    • 2
  • Jin-Soo Kim
    • 3
  • Samuel Moon-Ho Song
    • 4
  1. 1.Computer and Telecommunications Engineering DivisionYonsei Univ.GangwonKorea
  2. 2.School of Electrical and Computer EngineeringPurdue Univ.West LafayetteUSA
  3. 3.Division of Information Communication and Computer EngineeringHanbat Univ.DaejeonKorea
  4. 4.School of Mechanical and Aerospace EngineeringSeoul National Univ.SeoulKorea

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