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A shot detection technique using linear regression of shot transition pattern

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Abstract

Video segmentation acts as the fundamental step for various applications like, archiving, content based retrieval, copy detection and summarization of video data. Shot detection is first level of segmentation. In this work, a shot detection methodology is presented that evolves around a simple shot transition model based on the similarity of the frames with respect to a reference frame. Frames in an individual shot are very similar in terms of their visual content. Whenever a shot transition occurs a change in similarity values appears. For an abrupt transition, the rate of change is very high, while for gradual it is not so apparent. To overcome the effect of noise in similarity values, line is fit over a small window using a linear regression. Thus slope of this line exhibits the underlying pattern of transition. A novel algorithm for shot detection, hence, is developed based on the variation pattern of the similarity values of the frames with respect to a reference frame. First an algorithm is proposed, which is direct descendant of the underlying transition model and applies a threshold on the similarity values to detect the transitions. Then this algorithm is improved by utilizing the slope of linear approximation of variation in similarity values rather than the absolute values, following least square regression. Threshold on the slope is determined with a bias towards minimizing false rejection rate at the cost of false acceptance rate. Finally, a simple post-processing technique is adopted to reduce the false detection. Experiment is done with the video sequences taken from TRECVID 2001 database, action type movie video, recorded sports and news video. Comparison with few other systems indicates that the performance of the proposed scheme is quite satisfactory.

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References

  1. Adjeroh D, Lee MC, Banda N, Kandaswamy U (2009) Adaptive edge-oriented shot boundary detection. EURASIP J Image Video Process 2009:5:1–5:31

    Article  Google Scholar 

  2. Amel AM, Abdessalem BA, Abdellatif M (2010) Video shot boundary detection using motion activity descriptor. J Telecommun 2(1):54–59

    Google Scholar 

  3. Amiri A, Fathy M (2009) Video shot boundary detection using generalized eigenvalue decomposition and gaussian transition detection. In: Proceedings of the international conference on computational science and its applications, pp 780–790

    Google Scholar 

  4. Bescos J, Cisneros G, Martinez JM, Menendez JM, Cabrera J (2005) A unified model for techniques on video-shot transition detection. IEEE Trans Multimed 7(2):293–307

    Article  Google Scholar 

  5. Bhattacharyya A (1943) On a measure of divergence between two statistical populations defined by their probability distributions. Bull Calcutta Math Soc 35:99–109

    MathSciNet  MATH  Google Scholar 

  6. Cernekova Z, Pitas I, Nikou C (2006) Information theory-based shot cut/fade detection and video summarization. IEEE Trans CSVT 16(1):82–91

    Google Scholar 

  7. Chen LH, Lai YC, Liao HYM (2008) Movie scene segmentation using background information. Pattern Recognit 41(3):1056–1068

    Article  MATH  Google Scholar 

  8. Cooper M, Foote J (2005) Discriminative techniques for keyframe selection. In: Proceedings of the ICME, The Netherlands, pp 502–505

    Google Scholar 

  9. Grana C, Cucchiara R (2007) Linear transition detection as a unified shot detection approach. IEEE Trans CSVT 17(4):483–489

    Google Scholar 

  10. Hampapur A, Jain R, Weymouth T (1995) Production model based digital video segmentation. Multimed Tools Appl 1:1–38

    Article  Google Scholar 

  11. Haoran Y, Rajan D, Chia LT (2006) A motion-based scene tree for browsing and retrieval of compressed video. Inf Syst 31(7):638–658

    Article  Google Scholar 

  12. Huan Z, Xiuhuan L, Lilei Y (2008) Shot boundary detection based on mutual information and canny edge detector. In: Proceedings of the international conference on computer science and software engineering, pp 1124–1128

    Google Scholar 

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

    Google Scholar 

  14. Le DD, Satoh S, Ngo TD, Duong DA (2008) A text segmentation based approach to video shot boundary detection. In: Proceedings of multimedia signal processing, pp 702–706

    Google Scholar 

  15. Ling X, Chao H, Huan L, Zhang X (2008) A general method for shot boundary detection. In: Proceedings of the international conference on multimedia and ubiquitous engineering, pp 394–397

    Google Scholar 

  16. Liu X, Chen T (2002) Shot boundary detection using temporal statistics modelling. In: Proceedings of the ICASSP, pp 3389–3392

    Google Scholar 

  17. Mas J, Fernandez G (2003) Video shot boundary detection based on color histogram. Notebook Papers TRECVID2003

  18. Mohanta PP, Saha SK, Chanda B (2012) A model-based shot boundary detection technique using frame transition parameters. IEEE Trans Multimed 14(1):223–233

    Article  Google Scholar 

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

    Google Scholar 

  20. Patel NV, Sethi IK (1997) Video shot detection and characterization for video databases. Pattern Recognit 30(4):583–592

    Article  Google Scholar 

  21. Porter S, Mirmehdi M, Thomas B (2001) Detection and classification of shot transitions. In: Proceedings of the 12th British machine vision conference. BMVA Press, pp 73–82

    Google Scholar 

  22. Rees DG (1987) Foundations of statistics. CRC Press

  23. Smeaton AF, Over P, Doherty AR (2010) Video shot boundary detection: seven years of trecvid activity. Comput Vis Image Underst 114(4):411–418

    Article  Google Scholar 

  24. Tsamoura E, Mezaris V, Kompatsiaris I (2008) Gradual transition detection using color coherence and other criteria in a video shot meta-segmentation framework. In: Proceedings of the ICIP, pp 45–48

    Google Scholar 

  25. Yoo HW, Ryoo HJ, Jang DS (2006) Gradual shot boundary detection using localized edge blocks. Multimed Tools Appl 28:283–300

    Article  Google Scholar 

  26. Yuan J, Zheng W, Chen L, Ding D, Wang D, Tong Z, Wang H, Wu J, Li J, Lin F, Zhang B (2004) Tsinghua university at trecvid 2004: shot boundary detection and high-level feature extraction. In: Proceedings of the TREC Video Retrieval Evaluation (TRECVID), pp 84–196

    Google Scholar 

  27. Zhang C, Wang W (2012) A robust and efficient shot boundary detection approach based on fisher criterion. In: Proceedings of the ACM international conference on multimedia, pp 701–704

    Chapter  Google Scholar 

  28. Zhang W, Lin J, Chen X, Huang Q, Liu Y (2006) Video shot detection using hidden markov models with complementary features. In: Proceedings of the international conference on innovative computing, information and control, pp 593–596

    Google Scholar 

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Authors and Affiliations

  1. Tirthapati Institution, Kolkata, India

    Debabrata Dutta

  2. CSE Department, Jadavpur University, Kolkata, India

    Sanjoy Kumar Saha

  3. ECS Unit, Indian Statistical Institute, Kolkata, India

    Bhabatosh Chanda

Authors
  1. Debabrata Dutta
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  2. Sanjoy Kumar Saha
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  3. Bhabatosh Chanda
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Correspondence to Sanjoy Kumar Saha.

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Dutta, D., Saha, S.K. & Chanda, B. A shot detection technique using linear regression of shot transition pattern. Multimed Tools Appl 75, 93–113 (2016). https://doi.org/10.1007/s11042-014-2273-y

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  • DOI: https://doi.org/10.1007/s11042-014-2273-y

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