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Novel Research in the Field of Shot Boundary Detection – A Survey

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Advances in Intelligent Informatics

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 320))

Abstract

Segregating a video sequence into shots is the first step toward video-content analysis and content-based video browsing and retrieval. A shot may be defined as a sequence of consecutive frames taken by a single uninterrupted camera. Shots are the basic building blocks of videos and their detection provides the basis for higher level content analysis, indexing and categorization. The problem of detecting where one shot ends and the next begins is known as Shot Boundary Detection (SBD). Over the past two decades, numerous SBD techniques have been proposed in the literature. This paper presents a brief survey of all the major novel and latest contributions in this field of digital video processing.

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References

  1. Yuan, J., Wang, H., Xiao, L., et al.: A formal study of shot boundary detection. IEEE Trans. Circuits Syst. Video Technol. 17(2), 168–186 (2007)

    Article  Google Scholar 

  2. Kasturi, R., Jain, R.: Dynamic vision. In: Kasturi, R., Jain, R. (eds.) Computer Vision: Principles. IEEE Computer Society Press (1991)

    Google Scholar 

  3. Smeaton, A.F., Over, P., Doherty, A.R.: Video shot boundary detection: Seven years of TRECVid activity. Comput. Vis. Image Underst. 114(4), 411–418 (2010)

    Article  Google Scholar 

  4. Jiang, H., Lin, T., Zhang, H.: Video segmentation with the support of audio segmentation and classification. In: IEEE Int. Conf. Multimed. Expo., New York, vol. 3, pp. 1551–1554 (2000)

    Google Scholar 

  5. Javed, O., Khan, S., Rasheed, Z., et al.: A framework for segmentation of interview videos. In: Int. Conf. Internet Multimed. Syst. Appl., Las Vegas (2000)

    Google Scholar 

  6. Sze, K.W., Lam, K.M., Qiu, G.: Scene cut detection using the colored pattern appearance model. In: IEEE Int. Conf. Image Process., Spain, vol. 2, pp. 1017–1020 (2003)

    Google Scholar 

  7. Urhan, O., Güllü, M.K., Ertürk, S.: Modified phase-correlation based robust hardcut detection with application to archive film. IEEE Trans. Circuits Syst. Video Technol. 16(6), 753–770 (2006)

    Article  Google Scholar 

  8. Liu, T.Y., Lo, K.T., Zhang, X.D., et al.: A new cut detection algorithm with constant false-alarm ratio for video segmentation. J. Vis. Commun. Image Represent. 15(2), 132–144 (2004)

    Article  Google Scholar 

  9. Whitehead, A., Bose, P., Laganiere, R.: Feature based cut detection with automatic threshold selection. In: 3rd Int. Conf. Content Based Image Video Retr., Ireland (2004)

    Google Scholar 

  10. Urhan, O., Güllü, M.K., Ertürk, S.: Shot-cut detection for B&W archive films using best-fitting kernel. Int. J. Electron. Commun. 61(7), 463–468 (2007)

    Article  Google Scholar 

  11. Chen, L.H., Lai, Y.C., Liao, H.M.: Movie scene segmentation using background information. Pattern Recognit. 41(3), 1056–1065 (2008)

    Article  MATH  Google Scholar 

  12. Krulikovská, L., Polec, J.: Shot Detection using modified Dugad model. World Academy of Sci. Eng. Technol. 6 (2012)

    Google Scholar 

  13. Truong, B.T., Dorai, C., Venkatesh, S.: Improved fade and dissolve detection for reliable video segmentation. In: IEEE Int. Conf. Image Process., Vancouver, vol. 3, pp. 961–964 (2000)

    Google Scholar 

  14. Nam, J., Tewfik, A.: Detection of gradual transitions in video sequences using b-spline interpolation. IEEE Trans. Multimed. 7(4), 667–679 (2005)

    Article  Google Scholar 

  15. Tsamoura, E., Mezaris, V., Kompatsiaris, I.: Gradual transition detection using color coherence and other criteria in a video shot meta-segmentation framework. In: IEEE Int. Conf. Image Process. Multimed. Image Retr., California, pp. 45–48 (2008)

    Google Scholar 

  16. Sidiropoulos, P., Mezaris, V., Kompatsiaris, I., et al.: Temporal video segmentation to scenes using high-level audio-visual features. IEEE Trans. Circuits Syst. Video Technol. 21(8), 1163–1177 (2011)

    Article  Google Scholar 

  17. Huang, C.L., Liao, B.Y.: A robust scene-change detection method for video segmentation. IEEE Trans. Circuits Syst. Video Technol. 11(12), 1281–1288 (2001)

    Article  Google Scholar 

  18. Li, W.K., Lai, S.H.: Integrated video shot segmentation algorithm. In: SPIE Conf. Storage Retr. Media Databases, California, pp. 264–271 (2003)

    Google Scholar 

  19. Lelescu, D., Schonfeld, D.: Statistical sequential analysis for real-time video scene change detection on compressed multimedia bitstream. IEEE Trans. Multimed. 5(1), 106–117 (2003)

    Article  Google Scholar 

  20. Zheng, W., Yuan, J., Wang, H., et al.: A novel shot boundary detection framework. In: SPIE Vis. Commun. Image Process., vol. 5960, pp. 410–420 (2005)

    Google Scholar 

  21. Fang, H., Jiang, J., Feng, Y.: A fuzzy logic approach for detection of video shot boundaries. Pattern Recognit. 39, 2092–2100 (2006)

    Article  MATH  Google Scholar 

  22. Grana, C., Cucchiara, R.: Linear transition detection as a unified shot detection approach. IEEE Trans. Circuits Syst. Video Technol. 17(4), 483–489 (2007)

    Article  Google Scholar 

  23. Amiri, A., Fathy, M.: Video shot boundary detection using generelized Eigen value decomposition and Gaussian transition detection. Comput. Inform. 30(3), 595–619 (2011)

    Google Scholar 

  24. Jiang, X., Sun, T., Liu, J., et al.: An adaptive video shot segmentation scheme based on dual-detection model. Neurocomput. 116, 101–111 (2013)

    Article  Google Scholar 

  25. Hampapur, A., Jain, R.C., Weymouth, T.: Production model based digital video segmentation. Int. J. Multimed. Tools Appl. 1(1), 9–46 (1995)

    Article  Google Scholar 

  26. Lo, C., Wang, S.J.: Video segmentation using a histogram-based fuzzy C-means clustering algorithm. In: IEEE Int. Fuzzy Syst. Conf., vol. 3, pp. 920–923 (2001)

    Google Scholar 

  27. Damnjanovic, U., Izquierdo, E., Grzegorzek, M.: Shot boundary detection using spectral clustering. In: Eur. Signal Process. Conf., Poland, pp. 1779–1783 (2007)

    Google Scholar 

  28. Boccignone, G., Chianese, A., Moscato, V., et al.: Foveated shot detection for video segmentation. IEEE Trans. Circuits Syst. Video Technol. 15(3), 365–377 (2005)

    Article  Google Scholar 

  29. Han, B., Hu, Y., Wang, G., et al.: Enhanced sports video shot boundary detection based on middle level features and a unified model. IEEE Trans. Consum. Electron. 53(3), 168–1176 (2007)

    Article  MathSciNet  Google Scholar 

  30. Chasanis, V., Likas, A., Galatsanos, N.: Simultaneous detection of abrupt cuts and dissolves in videos using support vector machines. Pattern Recognit. Lett. 30(1), 55–65 (2009)

    Article  Google Scholar 

  31. Oh, J.H., Hua, K.A., Liang, N.: A content-based scene change detection and classification technique using background tracking. In: SPIE Conf. Multimed. Comput. Netw., California, vol. 3969, pp. 254–265 (2000)

    Google Scholar 

  32. Li, Z.N., Zhong, X., Drew, M.S.: Spatial temporal joint probability images for video segmentation. Pattern Recognit. 35(9), 1847–1867 (2002)

    Article  MATH  Google Scholar 

  33. Guimarães, S.J.F., Couprie, M., de A Araújo, A., et al.: Video segmentation based on 2D image analysis. Pattern Recognit. Lett. 24(7), 947–957 (2003)

    Article  Google Scholar 

  34. Zhao, R., Grosky, W.I.: Video shot detection using color anglogram and latent semantic indexing: From contents to semantics. In: Furht, B., Marques, O. (eds.) Handbook of Video Databases: Design and Applications, pp. 371–392. CRC Press (2003)

    Google Scholar 

  35. Albanese, M., Chianese, A., Moscato, V., et al.: A formal model for video shot segmentation and its application via animate vision. Multimed. Tools Appl. 24(3), 253–272 (2004)

    Article  Google Scholar 

  36. Mendi, E., Bayrak, C.: Shot boundary detection and key frame extraction using salient region detection and structural similarity. In: 48th ACM Southeast Conf., Massachusetts (2010)

    Google Scholar 

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Authors and Affiliations

  1. UIET, Panjab University, Chandigarh, India

    Raahat Devender Singh & Naveen Aggarwal

Authors
  1. Raahat Devender Singh
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  2. Naveen Aggarwal
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Correspondence to Raahat Devender Singh .

Editor information

Editors and Affiliations

  1. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

  2. Indian Institute of Information Technology and Management- - Kerala (IIITM-K), Trivandrum, Kerala, India

    Sabu M. Thampi

  3. Faculty of Design, Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  4. Department of Information Systems and Operations Management, University of Florida, Warrington College of Business, Florida, Florida, USA

    Selwyn Piramuthu

  5. University of Applied Sciences, Institute for Information Systems, Olten, Switzerland

    Thomas Hanne

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Singh, R.D., Aggarwal, N. (2015). Novel Research in the Field of Shot Boundary Detection – A Survey. In: El-Alfy, ES., Thampi, S., Takagi, H., Piramuthu, S., Hanne, T. (eds) Advances in Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 320. Springer, Cham. https://doi.org/10.1007/978-3-319-11218-3_41

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  • DOI: https://doi.org/10.1007/978-3-319-11218-3_41

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11217-6

  • Online ISBN: 978-3-319-11218-3

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