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Abrupt Scene Change Detection Using Block Based Local Directional Pattern

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Data Management, Analytics and Innovation

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1016))

Abstract

In the present communication an ingenious and robust method of abrupt scene change detection in video sequences in presence of illumination variation based on local directional pattern is proposed. A similarity measure is developed by evaluating the difference between the new texture based feature descriptor which is compared to an automatically generated global threshold for evaluation of the scene change detection. The proposed framework is tested on few publicly available videos and TRECVid dataset. The encouraging results are in favor of the credibility of the proposed framework.

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References

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

    Article  Google Scholar 

  2. Gargi, U., Kasturi, R., & Strayer, S. (2000). Performance characterisation of video shot change detection methodes. IEEE Transactions on Circuits and Systems for Video Technology, 10(1), 1–13.

    Google Scholar 

  3. Cotsaces, C., Nikolaidis, N., Pitas, I. (2006). Video shot detection and condensed representation. a review. IEEE Signal Processing Magazine, 23(2), 28–37.

    Article  Google Scholar 

  4. Koprinska, I., & Carrato, S. (2001). Temporal video segmentation: A survey. Signal Processing: Image Communication, 16(5), 477–500. January.

    Google Scholar 

  5. Hanjalic, A. Shot boundary detection: Unraveled and resolved. IEEE Transactions on Circuits and Systems for Video Technology, 12(2), 90–105.

    Article  Google Scholar 

  6. Boreczky, J. S., & Rowe, L. A. (1996). Comparison of video shot boundary detection techniques. Journal of Electronic Imaging, 5(2), 122–128. April.

    Article  Google Scholar 

  7. Yoo, H. W., Ryoo, H. J., & Jang, D. S. (2006). Gradual shot boundary detection using localised edge blocks. Multimedia Tools and Applications, 28(3), 283–300. March.

    Article  Google Scholar 

  8. Ling, X., Chao, L., Huan, L., & Zhang, X. (2008) A general method for shot boundary detection. Proceedings of International Conference of Multimedia and Ubiqutous Engineering (pp. 394–397), 24–26 April 2008, Busan, Korea.

    Google Scholar 

  9. Adjeroh, D., Lee, M. C., Banda, N., & Kandaswamy, U. (2009). Adaptive edge-oriented shot boundary detection. EURASIP Journal on Image and Video Processing.

    Google Scholar 

  10. Zabih R., Miller, J., & Mai, K. (1995). A feature based algorithm for detecting and classifying scene breaks. In Proceedings of the Third ACM International Conference on Multimedia (pp. 189–200), San Francisco, California, USA.

    Google Scholar 

  11. Amel, A. M., Abdessalem, B. A., & Abdellatif, M. (2010). Video shot boundary detection using motion activity descriptor. Journal of Telecommunication, 2(1), 54–59. April.

    Google Scholar 

  12. Murai, Y., & Fujiyoshi, H. (2008). Shot boundary detection using co-occurrence of global motion in video stream. In Proceedings of the 19th ICPR (pp. 1–4). 23 January 2009.

    Google Scholar 

  13. Kawai, Y., Sumiyoshi, H., & Yagi, N. Shot boundary detection at TRECVid 2007. In Proceedings of the TRECVID Workshop (pp. 1–8).

    Google Scholar 

  14. Lian, Shiguo. (2011). Automatic video temporal segmentation based on multiple features. Soft Computing, 15(3), 469–482. March.

    Article  Google Scholar 

  15. Lakshmi Priya, G. G., & Domnic, S. (2014). Walsh-Hadamard transform kernel-based feature vector for shot boundary detection. IEEE Transactions on Image Processing, 23(12), 5187–5197.

    Google Scholar 

  16. Chasanis, V., Likas, A., & Galatsanos, N. (2009). Simultaneous detection of abrupt cuts and dissolves in videos using support vector mechines. Pattern Recognition Letters, 30(1), 55–65.

    Article  Google Scholar 

  17. Kar, T., Kanungo, P. (2015). A texture based method for scene change detection. 2015 IEEE Power, Communication and Information Technology Conference (PCITC) (pp. 72–77), 15–17 October, India.

    Google Scholar 

  18. Kar, T., & Kanungo, P. (2015). Cut detection using block based centre symmetric local binary pattern. In 2015 International Conference on Man and Machine Interfacing (MAMI) (pp. 1–5). 17–19 December 2015.

    Google Scholar 

  19. Ojala, T., Pietikainen, M., & Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern analysis and machine intelligence, 24(7), 971–987.

    Google Scholar 

  20. Chakraborti, T., McCane, B., Mills, S., & Pal, U. (2018). Loop descriptor: Local optimal oriented pattern. IEEE Signal Processing Letters, 25(5), 635–639.

    Article  Google Scholar 

  21. Jabid, T., Kabir, M. H., & Chae. O. (2010). Gender classification using local directional pattern (LDP). 2010 International Conference on Pattern Recognition, Istanbul, Turkey (pp. 2162–2165), August 2010.

    Google Scholar 

  22. The open video project. http://www.open-video.org. Accessed March 2014.

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Author information

Authors and Affiliations

  1. KIIT Deemed to be University, Bhubaneswar, Odisha, India

    T. Kar

  2. C. V. Raman College of Engineering, Bhubaneswar, Odisha, India

    P. Kanungo

Authors
  1. T. Kar
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  2. P. Kanungo
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Corresponding author

Correspondence to T. Kar .

Editor information

Editors and Affiliations

  1. Society for Data Science, Pune, Maharashtra, India

    Neha Sharma

  2. A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata, West Bengal, India

    Amlan Chakrabarti

  3. Department of Automatics and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

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Kar, T., Kanungo, P. (2020). Abrupt Scene Change Detection Using Block Based Local Directional Pattern. In: Sharma, N., Chakrabarti, A., Balas, V. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 1016. Springer, Singapore. https://doi.org/10.1007/978-981-13-9364-8_15

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  • DOI: https://doi.org/10.1007/978-981-13-9364-8_15

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9363-1

  • Online ISBN: 978-981-13-9364-8

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