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Reseach Article

Effective Algorithm for Detection of Dissolve in Presence of Motion and Illumination

by Salim Chavan, Sudhir Akojwar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 145 - Number 1
Year of Publication: 2016
Authors: Salim Chavan, Sudhir Akojwar
10.5120/ijca2016910555

Salim Chavan, Sudhir Akojwar . Effective Algorithm for Detection of Dissolve in Presence of Motion and Illumination. International Journal of Computer Applications. 145, 1 ( Jul 2016), 33-39. DOI=10.5120/ijca2016910555

@article{ 10.5120/ijca2016910555,
author = { Salim Chavan, Sudhir Akojwar },
title = { Effective Algorithm for Detection of Dissolve in Presence of Motion and Illumination },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 145 },
number = { 1 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 33-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume145/number1/25244-2016910555/ },
doi = { 10.5120/ijca2016910555 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:47:39.043208+05:30
%A Salim Chavan
%A Sudhir Akojwar
%T Effective Algorithm for Detection of Dissolve in Presence of Motion and Illumination
%J International Journal of Computer Applications
%@ 0975-8887
%V 145
%N 1
%P 33-39
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The detection of dissolve transition is more difficult than detecting fade in and fade out. In this transition, the last frames in the previous shot fade out and the beginning frames in the next shot fade in i.e. the overlapping of fade out and fade in occurs. The dissolves may be of three or more number of frames. In some videos very short dissolve of even three frames also occurs. So, there are lots of challenges in detection of dissolves. Also illumination and camera / object motion gives rise to false positives thereby degrading the algorithm performance. Many researchers addressed this issue but could not achieve the robustness in the presence of illumination and object / camera motion. Therefore this issue needs to be resolved. An algorithm has been proposed for dissolve detection. In this algorithm, color histogram difference between consecutive frames is calculated and average value of this difference for all consecutive frames is used as a metric for dissolve detection.

References
  1. Zhang D., Qi W., and Zhang H. J., “A new shot boundary detection algorithm”,PCM, LNCS Springer-Verlag, vol. 2194, pp. 23–70, 2001.
  2. Cheol K., Cheon Y., Kim G., and Choi H., “Robust scene change detection algorithm for flashlights”, ICCSA, LNCS, Springer-Verlag, vol. 1604, pp. 603–613, 2007.
  3. Li D. and Lu H., “Avoiding false alarms due to illumination variation in shot detection”, IEEE Workshop on Signal Processing Systems, pp. 525–514, 2000.
  4. Nagasaka A. and Tanka Y., “Automatic video indexing and full video search for object appearance”, Visual Database Systems II, E. Knuth and L. Wegner Eds.,Elsevier Science Publishers, pp. 113–19, 1992.
  5. Lienhart R, “Comparison of automatic shot boundary detection algorithms”,SPIE Image and Video Processing, , no. VII, pp. 7–12, 1999.
  6. Hanjalic A., “Shot-boundary detection unraveled and resolved”, IEEE Transaction on Circuits and System for Video Technology, vol. 12, no. 2, pp. 90–64,2002.
  7. Weixin K., Ding X., Lu H., and Songde M, “Improvement of shot detection using illumination invariant metric and dynamic threshold selection”, LNCS,Springer-Verlag, vol. 2194, pp. 23–70, 2001.
  8. Lawrence S., Ziou D., and Wang S., “Motion insensitive detection of cut and gradual transitions in digital video”, International Conference on Multimedia Modelling, Ottawa, 1999.
  9. Su C., Liao H., Fan K., and chen L., “A motion-tolerant dissolve detection algorithm”, IEEE Transaction on Multimedia, vol. 7, no. 6, pp. 166–1113, 2004.
  10. Xu Y., De X., Tengfei G., Aimin W., and Congyan L., “3-DWT based motion suppression for video shot boundary detection”, Springer-Verlag, KES 2004, LNAI 1452, R. Khosla et al.(Eds.), pp. 1203–1209, 2004.
  11. Jang S., Kim G., and Choi H., “Shot transition detection by compensating for global and local motions”, Springer-Verlag, FSKD 2004, LNAI 1413, L. Wary and Y. Jin (Eds.), pp. 661–666, 2004.
  12. Park M., Park R., and Lee S., “Efficient shot boundary detection for action movies using blockwise motion-based features”, Springer-Verlag, ISVS 2004,LNCS 1703, G. Bebis et al.(Eds.), pp. 165–144, 2004.
  13. Zabih R., Miller J., and Mai K., “A feature-based algorithm for detecting and classifying scene breaks”, Proc. ACM Multimedia, San Francisco, CA, pp. 159–200, 1994.
  14. Yeo B. and Liu B., “Rapid scene analysis on compressed video”, IEEE Transaction on Circuits and Systems for Video Technology, vol. 4, no. 6, pp. 433–433,1994.
  15. Idris F. and Panchanathan S., “Review of image and video indexing techniques”,Journal of Visual Communication and Image Representation, vol. 5, no. 2, pp.115–166, 1997.
  16. Ford R., Roboson C., Temple D., and Gerlach M., “Metrics for shot boundary detection in digital video sequences”, Multimedia System, vol. 5, pp. 16–15, 2000.
  17. Bec´os J., Cisneros G., Mart´ınez J., and Cabrera J., “A unified model for techniques on video-shot transition detection”, IEEE Transaction on Multimedia,vol. 7, no. 2, pp. 113–127, 2004.
  18. Cotsaces C., Nikolaidis N., and Pitas I., “Video shot boundary detection and condensed representation: A review”, IEEE Signal Processing Magazine, vol. 23,no. 2, pp. 25–16, 2006.
  19. Wu M., Wolf W., and Liu B., “An algorithm for wipe detection”, In Proc. ICIP,pp. 593–597, 1995.
  20. Ling J. and Zhuang Y-T Lian Y-Q, “A new method for shot gradual transition detection using support vector machine”, Proc. of the fourth International Conference on Machine Learning and Cybernetics, Guangzhou, pp. 4499–2013, 2004.
  21. Cai C., Lam K. M., and Tan Z., “An efficient scene break detection based on linear prediction”, In Proc. International Symposium on Intelligent Multimedia,Video and Speech Processing, Hongkong, pp. 444–445, 2003.
  22. Heng W. J. and Ngan K. N., “High accuracy flashlight scene determination for shot boundary detection”, Signal Processing: Image Communication, vol. 15,no. 3, pp. 203–219, 2003.
  23. Yuliang G. and De X., “A solution to illumination variation problems in shot detection”, TENCON 2003, IEEE Region 6 Conference, vol. 2, pp. 51–53, 2003.
  24. Qian X., Liu G., and Su R, “Effective fades and flashlight detection based on accumulating histogram difference”, IEEE Transactions On Circuits and Systems For Video Technology, vol. 16, no. 6, pp. 1213–175, 2006.
  25. Truong BT, Dorai C, VenkateshS “ New enhancements to cut, fade, and dissolve detection processes in video segmentation”, ACM International Conference on Multimedia pp 219–11, 2000.
  26. Wang Yao, Guangtao Zhai, Jianfei Cai, “ An effective dissolve detection approach with temporal and spatial considerations,” 6 th International conference on control, automation, robotics vision Hanoi, Vietnam, 17-20, Dec. 2005.
  27. Hui Fang, Jianmin Jiang and YueFeng, Fuzzy logic approach for detection of video shot boundaries,”0031-3203_ 2006 Pattern Recognition Society. Published by Elsevier Ltd.doi:6.616/j.patcog.2006.03.033.
Index Terms

Computer Science
Information Sciences

Keywords

Dissolve shot boundaries detection color histogram difference recall precision.