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A novel video shot boundary detection framework employing DCT and pattern matching

  • Shekhar Dhiman
  • Rashmi ChawlaEmail author
  • Shailender Gupta
Article
  • 4 Downloads

Abstract

The video Shot Boundary Detection (SBD) is an elementary step in realising a system capability to perform content based video search, structural analysis, data retrieval and video summation. Myriad research works in the past have been reported to construct SBD algorithms. However, the need of an error-free, meticulous and cost-effective SBD technique still persists; for applications viz. apt management, storage, browsing, video indexing and retrieval of multimedia data. This paper is an effort in the same direction with the aim of achieving high execution speed and greater accuracy. The proposed SBD technique in this paper incorporates three steps: (i) Candidate Segment Selection (ii) Cut Transition detection (iii) Gradual Transition detection. This paper adopts pixel based technique with candidate segment selection to speed up the SBD. For Cut Transition detection, the proposed method employs Discrete Cosine Transform (DCT) and for Gradual Transition detection, it employs Image Histogram and Pattern Matching. The comparison of MATLAB simulation results of the proposed SBD technique with those in literature manifest better results in terms of execution speed and accuracy.

Keywords

Fast Shot Boundary detection Discrete Cosine Transform (DCT) Adaptive Threshold Candidate segment Cut Transition and Gradual Transition detection Cosine Distance 

Notes

References

  1. 1.
    Hu W, Xie N, Li L, Xeng X, Maybank S (Nov. 2011) A Survey on Visual Content-Based Video Indexing and Retrieval. Systems, Man and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on 41(6):797–819CrossRefGoogle Scholar
  2. 2.
    Chawla R, Singal P, Garg AK (2018) A Mamdani Fuzzy Logic System to Enhance Solar Cell Micro-Cracks Image Processing. 3D Res 9(34):1–12Google Scholar
  3. 3.
    Bi C et al (2018) Dynamic Mode Decomposition Based Video Shot Detection. IEEE Access 6:21397–21407CrossRefGoogle Scholar
  4. 4.
    Liang R, Zhu Q, Wei H, Liao S (2017) A Video Shot Boundary Detection Approach Based on CNN Feature, 2017 IEEE International Symposium on Multimedia (ISM), Taichung, 489-494Google Scholar
  5. 5.
    HuH JH, Seo K (2017) An indoor location-based control system using bluetooth beacons for IoT systems. Sensors vol.17, no.12Google Scholar
  6. 6.
    Cotsaces C, Nikolaidis N, Pitas I (2006) Video shot detection and condensed representation, a review. Signal Processing Magazine, IEEE 23:28–37CrossRefGoogle Scholar
  7. 7.
    Esponda F, Forrest S, Helman " P (2004) A formal framework for positive and negative detection schemes. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 34(1):357–373CrossRefGoogle Scholar
  8. 8.
    Lu ZM, Shi Y (2013) Fast video shot boundary detection based on SVD and pattern matching. IEEE Transactions on Image processing 22(12):5136–5145MathSciNetCrossRefGoogle Scholar
  9. 9.
    Lakshmi Priya GG, Domnic S (2014) Walsh-Hadamard Transform Kernal-Based Feature vector for Shot Boundary Detection. Image Processing, IEEE Transactions 23:5187–5197CrossRefzbMATHGoogle Scholar
  10. 10.
    Li YN, Lu ZM, Niu XM (2009) Fast video shot boundary detection framework employing pre-processing techniques. Image Processing, IET 3:121–134CrossRefGoogle Scholar
  11. 11.
    Sun J, Wan Y (2014) A novel metric for efficient video shot boundary detection, in 2014 IEEE Visual Communications and Image Processing Conference, 45-48Google Scholar
  12. 12.
    Lu ZM, Shi Y (2013) Fast Video Shot Boundary Detection Based on SVD and Pattern Matching. Image Processing, IEEE Transactions 22:5136–5145MathSciNetCrossRefGoogle Scholar
  13. 13.
    Mas J, Fernandez G (2003) Video shot boundary detection based on color histogram, in Proc. TRECVID Google Scholar
  14. 14.
    Adjeroh D, Lee MC, Banda N, Kandaswamy U (2009) Adaptive edge-oriented shot boundary detection. EURASIP J. Image Video Process. 2009:1–13CrossRefGoogle Scholar
  15. 15.
    Apostolidis E, Mezaris V (2014) Fast Shot segmentation combining global and local visual descriptors, in Speech and Signal Processing (ICASSP), 2014 IEEE International conference on, 6583-6587Google Scholar
  16. 16.
    Cernekova Z, Kotropoulos C, Pitas I (2007) Video shot boundary detection using singular value decomposition and statistical tests. J. Electron Imaging 16(4):043012-1–043012-13CrossRefGoogle Scholar
  17. 17.
    Tippaya S, Sitjongsataporn S, Tan T, Khan MM, Chamnongthai K (2017) Multi-modal Visual Features based Video Shot Boundary Detection. Image Processing, IEEE Access on 5:12563–12575CrossRefGoogle Scholar
  18. 18.
    Tippaya S, Sitjongsataporn S, Tan T, Khan MM, Chamnongthai K (2015) Video shot boundary detection based on candidate segment selection and transition pattern analysis, in 2015 IEEE International Conference on Digital Signal Processing (DSP), 1025-1029Google Scholar
  19. 19.
    Shiyang L, Zhiyong W, Meng W, Ott M, Dagan F (2010) Adaptive reference frame selection for near duplicate video shot detection,” in Image Processing (ICIP), 17 th IEEE International Conference on, 2341-2344Google Scholar
  20. 20.
    Fang H, Jiang J, Feng Y (2006) A fuzzy logic approach for detection of video shot boundaries. Pattern Recognition 39:2092–2100CrossRefzbMATHGoogle Scholar
  21. 21.
    Bay H, Tuytelaars T, Gool LV (2006) SURF: Speeded Up Robust Features, ECCV 2006, vol. 1, pp. 404-417Google Scholar
  22. 22.
    Schafer RW (2011) What is a Savitzky-Golay Filter? [Lecture Notes]. IEEE Signal Processing Magazine 28:111–117CrossRefGoogle Scholar
  23. 23.
    Ren J, Jiang J, Chen J (2009) Shot boundary detection in MPEG Videos using local and global indicators. Circuits and systems for Video technology, IEEE transaction on 19:1234–1238CrossRefGoogle Scholar
  24. 24.
    Barjatya A (2004) Block matching algorithms for motion estimation. IEEE Trans. Evol. Comput. 8(3):225–239CrossRefGoogle Scholar
  25. 25.
    Video data set [Online], Available: http://www.open-video.org/, Accessed May 2018.
  26. 26.
    Shen R, Lin Y, Juang TT, Shen VRL, Lim SY (2018) Automatic Detection of Video Shot Boundary in Social Media Using a Hybrid Approach of HLFPN and Keypoint Matching. IEEE Transactions on Computational Social Systems 5(1):210–219CrossRefGoogle Scholar
  27. 27.
    Xu J, Song L, Xie R (2016) Shot boundary detection using convolutional neural networks, 2016 Visual Communications and Image Processing (VCIP), Chengdu, 1-4Google Scholar
  28. 28.
    Yang Z, Tian L, Li C (2017) A Fast Video Shot Boundary Detection Employing OTSU’s Method and Dual Pauta Criterion, 2017 IEEE International Symposium on Multimedia (ISM), Taichung, 583-586Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Shekhar Dhiman
    • 1
  • Rashmi Chawla
    • 1
    Email author
  • Shailender Gupta
    • 1
  1. 1.Department of Electronics EngineeringJ.C. Bose University of Science and Technology, YMCAFaridabadIndia

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