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Local Subspace-Based Denoising for Shot Boundary Detection

  • Xuefeng Pan
  • Yongdong Zhang
  • Jintao Li
  • Xiaoyuan Cao
  • Sheng Tang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5027)

Abstract

Shot boundary detection (SBD) has long been an important problem in content based video analyzing. In existing works, researchers proposed kinds of methods to analyze the continuity of video sequence for SBD. However, the conventional methods focus on analyzing adjacent frame continuity information in some common feature space. The feature space for content representing and continuity computing is seldom specialized for different parts of video content. In this paper, we demonstrate the shortage of using common feature space, and propose a denoising method that can effectively restrain the in-shot change for SBD. A local subspace specialized for every period of video content is used to develop the denoising method. The experiment results show the proposed method can remove the noise effectively and promote the performance of SBD.

Keywords

subspace denoising shot boundary detection SVD 

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References

  1. 1.
    Smoliar, S.W., Zhang, H.-J.: Content-based video indexing and retrieval. IEEE Multimedia 1(2), 62–72 (1994)CrossRefGoogle Scholar
  2. 2.
    Vasconcelos, A.L.: Statistical models of video structure for content analysis and characterization. IEEE Trans. Image Process. 9(1), 3–19 (2000)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Lienhart: Reliable transition detection in videos: a survey and practitioner’s guide. Int. J. Image Graph. 1(3), 469–486 (2001)CrossRefGoogle Scholar
  4. 4.
    Hanjalic: Shot boundary detection: unraveled and resolved? IEEE Trans. Circuits Syst. Video Technol. 12(2), 90–105 (2002)CrossRefGoogle Scholar
  5. 5.
    Albanese, A.C., Moscato, V., Sansone, L.: A formal model for video shot segmentation and its application via animate vision. Multimedia Tools Appl 24(3), 253–272 (2004)CrossRefGoogle Scholar
  6. 6.
    Bescós, G.C., Martínez, J.M., Menendez, J.M., Cabrera, J.: A unified model for techniques on video shot transition detection. IEEE Trans. Multimedia 7(2), 293–307 (2005)CrossRefGoogle Scholar
  7. 7.
    Yuan, J., Wang, H., Xiao, L., Zheng, W., Li, J., Lin, F., Zhang, B.: A Formal Study of Shot Boundary Detection. IEEE Trans. Circuits Syst. Video Technol. 17(2), 168–186 (2007)CrossRefGoogle Scholar
  8. 8.
    Kikukawa, S.K.: Development of an automatic summary editing system for the audio visual resources. Trans. IEICE J75-A(2), 204–212 (1992)Google Scholar
  9. 9.
    Choubey, K., Raghavan, V.V.: Generic and fully automatic content-based image retrieval using color. Pattern Recog. Lett. 18(11–13), 1233–1240 (1997)CrossRefGoogle Scholar
  10. 10.
    Zhang, J., Low, C.Y., Smoliar, S.W.: Video parsing and browsing using compressed data. Multimedia Tools Appl. 1(1), 89–111 (1995)CrossRefGoogle Scholar
  11. 11.
    Zabih, J.M., Mai, K.: A Feature-Based Algorithm for Detecting and Classifying Scene Breaks. In: Proc. ACM Multimedia 1995, San Francisco, CA, pp. 189–200 (1995)Google Scholar
  12. 12.
    Zabih, J.M., Mai, K.: A Feature-based Algorithm for Detecting and Classification Production Effects. Multimedia Systems 7, 119–128 (1999)CrossRefGoogle Scholar
  13. 13.
    Akutsu, Y.T., Hashimoto, H., Ohba, Y.: Video Indexing Using Motion Vectors. In: Proc. SPIE Visual Communications and Image Processing, vol. 1818, pp. 1522–1530 (1992)Google Scholar
  14. 14.
    Shahraray: Scene Change Detection and Content-Based Sampling of Video Sequences. In: Proc. SPIE Digital Video Compression, Algorithm and Technologies, vol. 2419, pp. 2–13 (1995)Google Scholar
  15. 15.
    Zhang, J., Kankanhalli, A., Smoliar, S.W.: Automatic Partitioning of Full-Motion Video. Multimedia Systems 1(1), 10–28 (1993)CrossRefGoogle Scholar
  16. 16.
    Bouthemy, M.G., Ganansia, F.: A unified approach to shot change detection and camera motion characterization. IEEE Trans. Circuits Syst. Video Technol. 9(7), 1030–1044 (1999)CrossRefGoogle Scholar
  17. 17.
    Gargi, R.K., Strayer, S.H.: Performance characterization of video-shot-change detection methods. IEEE Trans. Circuits Syst. Video Technol. 10(1), 1–13 (2000)CrossRefGoogle Scholar
  18. 18.
    Shi, J.M.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Machine Intell. 22(8), 888–905 (2000)CrossRefGoogle Scholar
  19. 19.
    Rasheed, M.S.: Detection and Representation of Scenes in Videos. IEEE Trans. Multimedia 7(6), 1097–1105 (2005)CrossRefGoogle Scholar
  20. 20.
    Ngo, W., Ma, Y.F., Zhang, H.J.: Video summarization and Scene Detection by Graph Modeling. IEEE Trans. Circuits Syst. Video Technol. 15(2), 296–305 (2005)CrossRefGoogle Scholar
  21. 21.
    Hu, S.: Digital Signal Processing, 2nd edn. Tsinghua University Press, Beijing (2003)Google Scholar
  22. 22.
    Černeková, I.P., Nikou, C.: Information theory-based shot cut/fade detection and video summarization. IEEE Trans. Circuits Syst. Video Technol. 16(1), 82–91 (2006)CrossRefGoogle Scholar
  23. 23.
    Min, W., Lu, K., He, X.: Locality pursuit embedding. Pattern Recognition 37, 781–788 (2004)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Xuefeng Pan
    • 1
    • 2
    • 3
  • Yongdong Zhang
    • 1
    • 2
  • Jintao Li
    • 1
    • 2
  • Xiaoyuan Cao
    • 1
    • 2
    • 3
  • Sheng Tang
    • 1
    • 2
  1. 1.Virtual Reality LaboratoryInstitute of Computing Technology, Chinese Academy of SciencesBeijingChina
  2. 2.The Key Laboratory of Intelligent Information ProcessingInstitute of Computing Technology, Chinese Academy of SciencesBeijingChina
  3. 3.Graduate School of Chinese Academy of SciencesBeijingChina

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