Video Shot Detection Using Cumulative Colour Histogram

  • B. H. Shekar
  • K. Raghurama Holla
  • M. Sharmila Kumari
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 222)


The shot boundary detection is the fundamental step in video indexing and retrieval. In this paper a new method for video shot boundary detection based on slope and y-intercept parameters of the straight line fitted to the cumulative plot of color histogram is proposed. These feature vectors are extracted from every video frames and the frame dissimilarity values are compared against a threshold to identify the cuts and fades present in the video sequence. Experiments have been conducted on TRECVID video database to evaluate the effectiveness of the proposed model. A comparative analysis with other models is also provided to reveal the superiority of the proposed model for shot detection.


Cumulative colour histogram Cut detection Fade detection Video segmentation Shot boundary detection 


  1. 1.
    Alattar A (1997) Detecting fade regions in uncompressed video sequences. In: IEEE international conference on acoustics, speech, and signal processing 1999 (ICASSP-97), vol 4. pp 3025–3028Google Scholar
  2. 2.
    Boreczky J, Rowe L (1996) Comparison of video shot boundary detection techniques. J Electron Imaging 5(2):122–128CrossRefGoogle Scholar
  3. 3.
    Del Bimbo A (1999) Visual information retrieval. Morgan Kaufmann Publishers Inc., San FranciscoGoogle Scholar
  4. 4.
    Fernando W, Canagarajah C, Bull D (1999) Automatic detection of fade-in and fade-out in video sequences. In: Proceedings of 1999 IEEE international symposium on circuits and systems (ISCAS’99), vol 4, pp 255–258Google Scholar
  5. 5.
    Fernando W, Canagararajah C, Bull D (2000) Fade-in and fade-out detection in video sequences using histograms. In: Proceedings of 2000 IEEE international symposium on circuits and systems (ISCAS 2000), Geneva, vol 4, pp 709–712Google Scholar
  6. 6.
    Guimarães SJF, Couprie M, Araújo AdA, Leite NJ ( 2003) Video segmentation based on 2d image analysis. Pattern Recogn Lett 24:947–957Google Scholar
  7. 7.
    Hanjalic A (2002) Shot-boundary detection: unraveled and resolved? IEEE Trans Circuits Syst Video Technol 12(2):90–105Google Scholar
  8. 8.
    Kong W, Ding X, Lu H, Ma S (1999) Improvement of shot detection using illumination invariant metric and dynamic threshold selection. In: Visual information and information systems. Springer, Berlin, pp 658–659Google Scholar
  9. 9.
    Koprinska I, Carrato S (2001) Temporal video segmentation: a survey. Signal Process: Image Commun 16(5):477–500CrossRefGoogle Scholar
  10. 10.
    Le DD, Satoh S, Ngo TD, Duong DA (2008) A text segmentation based approach to video shot boundary detection. In: 2008 IEEE 10th workshop on multimedia signal processing, pp 702–706Google Scholar
  11. 11.
    Li S, Lee MC (2005) An improved sliding window method for shot change detection. In: SIP’05, pp 464–468Google Scholar
  12. 12.
    Lian S (2011) Automatic video temporal segmentation based on multiple features. Soft Comput—A Fusion Found Methodol Appl 15:469–482Google Scholar
  13. 13.
    Priya GGL, Dominic S (2010) Video cut detection using dominant color features. In: Proceedings of the first international conference on intelligent interactive technologies and multimedia (IITM ’10). ACM, New York, pp 130–134Google Scholar
  14. 14.
    Shekar BH, Raghurama Holla K, Sharmila Kumari M (2011) Video cut detection using chromaticity histogram. Inter J Mach Intell 4(3):371–375Google Scholar
  15. 15.
    Smeaton AF, Over P, Doherty AR (2010) Video shot boundary detection: seven years of TRECVID activity. Comput Vis Image Understand 114(4):411–418CrossRefGoogle Scholar
  16. 16.
    Truong B, Dorai C, Venkatesh S (2000) Improved fade and dissolve detection for reliable video segmentation. In: Proceedings of 2000 IEEE international conference on image processing, vol 3, pp 961–964Google Scholar
  17. 17.
    Tudor B (2009) Novel automatic video cut detection technique using Gabor filtering. Comput Electr Eng 35(5):712–721Google Scholar
  18. 18.
    Yoo H, Ryoo H, Jang D (2006) Gradual shot boundary detection using localized edge blocks. Multimedia Tools Appl 28(3):283–300CrossRefGoogle Scholar
  19. 19.
    Yuan J, Wang H, Xiao L, Zheng W, Li J, Lin F, Zhang B (2007) A formal study of shot boundary detection. IEEE Trans Circuits Syst Video Technol 17(2):168–186Google Scholar
  20. 20.
    Yusoff Y, Christmas WJ, Kittler J (2000) Video shot cut detection using adaptive thresholding. In: Proceedings of the British machine vision conference 2000 (BMVC 2000), Bristol, 11–14 Sept 2000Google Scholar
  21. 21.
    Zabih R, Miller J, Mai K (1999) A feature-based algorithm for detecting and classifying production effects. Multimedia Syst 7:119–128CrossRefGoogle Scholar
  22. 22.
    Zhang H, Kankanhalli A, Smoliar SW (1993) Automatic partitioning of full-motion video. Multimedia Syst 1:10–28CrossRefGoogle Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  • B. H. Shekar
    • 1
  • K. Raghurama Holla
    • 1
  • M. Sharmila Kumari
    • 2
  1. 1.Department of Computer ScienceMangalore UniversityKonaje, MangaloreIndia
  2. 2.Department of Computer Science and EngineeringP. A. College of EngineeringMangaloreIndia

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