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SBD-Duo: a dual stage shot boundary detection technique robust to motion and illumination effect

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Abstract

In this paper, we propose a novel shot boundary detection technique using gradient and colour information. The gradient similarity and luminance distortion are calculated to measure the contrast and structural changes of each frame including luminance changes. In the proposed system, the effects of the changes in luminance and contrast-structure are integrated via an adaptive method to extract the possible transitions using an adaptive threshold across the videos. In the verification part, CIEDE2000 colour-difference values of the possible transition frames are compared for declaration of abrupt and gradual transitions. Our system takes effectively less computational time to detect abrupt and gradual transition for a video as compared with contemporary solutions. Our proposed system also gives dominate the performance as compared with latest techniques in terms of F1 score using TRECVid 2001 and 2007 selected dataset. We have performed a series of rigorous experimentation to validate our claims.

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References

  1. Abdulhussain SH, Ramli AR, Saripan MI, Mahmmod BM, Al-Haddad SAR, Jassim WA (2018) Methods and challenges in shot boundary detection: a review. Entropy 20(4):1–42. https://doi.org/10.3390/e20040214

    Article  Google Scholar 

  2. Apostolidis E, Mezaris V (2014) Fast shot segmentation combining global and local visual descriptors. In: International conference on speech signal process. IEEE, pp 6583–6587

  3. Baber J, Afzulpurkar N, Satoh S (2013) A framework for video segmentation using global and local features. Int J Pattern Recognit Artif Intell 27 (5):1355007. https://doi.org/10.1142/S0218001413550070

    Article  Google Scholar 

  4. Boreczky JS, Rowe LA (1996) Comparison of video shot boundary detection techniques. J Electron Imaging 2670:122–128

    Article  Google Scholar 

  5. Chakraborty S, Thounaojam DM (2019) A novel shot boundary detection system using hybrid optimization technique. Appl Intell 49(9):3207–3220

    Article  Google Scholar 

  6. Chu J, Guo Z, Leng L (2018) Object detection based on multi-layer convolution feature fusion and online hard example mining. IEEE Access 6:1–1

    Article  Google Scholar 

  7. Chu J, Tu X, Leng L, Miao J (2019) Double-channel object tracking with position deviation suppression. IEEE Access 8:856–866

    Article  Google Scholar 

  8. Ciocca G, Schettini R (2006) Dynamic storyboards for video content summarization. In: International workshop on multimedia information retrieval, pp 259–267

  9. Fu Q, Zhang Y, Xu L, Li H (2013) A method of shot-boundary detection based on hsv space. In: International conference on computational intelligence and security. IEEE, pp 219–223

  10. Gao XB, Han B, Ji HB (2005) A shot boundary detection method for news video based on rough sets and fuzzy clustering. In: Proceedings of the international conference on image analysis and recognition, ICIAR’05. Springer, Berlin, pp 231–238

  11. Gaurav S, Wu W, Dalal EN, Kanungo P (2005) The ciede2000 color-difference formula: implementation notes, supplementary test data, and mathematical observations. Color Res Appl 30(1):21–30

    Article  Google Scholar 

  12. Heng W, Ngan K (1999) The implementation of object-based shot boundary detection using edge tracing and tracking. In: International symposium on circuits and systems VLSI, vol 4. IEEE, pp 439–442

  13. Heng WJ, Ngan KN (2001) An object-based shot boundary detection using edge tracing and tracking. J Vis Commun Image Represent 12(3):217–239

    Article  Google Scholar 

  14. Huan Z, Xiuhuan L, Lilei Y (2008) Shot boundary detection based on mutual information and canny edge detector. In: International conference on computer science and software engineering, vol 2. IEEE, pp 1124–1128

  15. Jadon R, Chaudhury S, Biswas K (2001) A fuzzy theoretic approach for video segmentation using syntactic features. Pattern Recognit Lett 22(13):1359–1369. https://doi.org/10.1016/S0167-8655(01)00041-1

    Article  MATH  Google Scholar 

  16. Kar T, Kanungo P (2017) A motion and illumination resilient framework for automatic shot boundary detection. Signal Image Video Process 11:1–8

    Article  Google Scholar 

  17. Ko KC, Cheon YM, Kim GY, Choi HI (2007) Robust scene change detection algorithm for flashlights. In: Gervasi O, Gavrilova ML (eds) Computational science and its applications. Springer, Berlin, pp 1003–1013

  18. Lakshmi P, Domnic S (2014) Hadamard transform kernel-based feature vector for shot boundary detection. IEEE Trans Image Process 23(12):5187–5197

    Article  MathSciNet  Google Scholar 

  19. Lee MS, Yang YM, Lee S-W (2001) Automatic video parsing using shot boundary detection and camera operation analysis. Pattern Recognit 2:711–719

    Article  Google Scholar 

  20. Lienhart RW (1998) Comparison of automatic shot boundary detection algorithms. In: Storage and retrieval for image and video databases VII, vol 3656

  21. Liu A, Lin W, Narwaria M (2012) Image quality assessment based on gradient similarity. IEEE Trans Image Process 21:1500–1512

    Article  MathSciNet  Google Scholar 

  22. Lu ZM, Shi Y (2013) Fast video shot boundary detection based on svd and pattern matching. IEEE Trans Image Process 22(12):5136–5145

    Article  MathSciNet  Google Scholar 

  23. Mas J, Fernandez G (2003) Video shot boundary detection based on color histogram. National Institute of Standards and Technology (NIST)

  24. Ruxandra Tapu TZ (2012) Video segmentation and structuring for indexing applications. Int J Multimed Data Eng Manag (IJMDEM) 2:38–58

    Article  Google Scholar 

  25. Singh A, Thounaojam DM, Chakraborty S (2019) A novel automatic shot boundary detection algorithm: robust to illumination and motion effect. Signal Image Video Process 14:1–9

    Google Scholar 

  26. Smeaton AF, Over P, Doherty AR (2010) Video shot boundary detection: seven years of {TRECVid} activity. Comput Vis Image Underst 114(4):411–418. https://doi.org/10.1016/j.cviu.2009.03.011. Image and Video Retrieval Evaluation

    Article  Google Scholar 

  27. Sun X, Zhao L, Zhang M (2011) A novel shot boundary detection method based on genetic algorithm-support vector machine. In: 2011 Third international conference on intelligent human-machine systems and cybernetics, vol 1, pp 144–147. https://doi.org/10.1109/IHMSC.2011.41

  28. Thounaojam DM, Trivedi A, Manglem Singh K, Roy S (2014) A survey on video segmentation. Springer, India, pp 903–912. https://doi.org/10.1007/978-81-322-1665-0_91

    Google Scholar 

  29. Thounaojam DM, Khelchandra T, Singh KM, Roy S (2016) A genetic algorithm and fuzzy logic approach for video shot boundary detection. Intell Neurosci 2016:14–25

    Google Scholar 

  30. Thounaojam DM, Bhadouria VS, Roy S, Singh KM (2017) Shot boundary detection using perceptual and semantic information. Int J Multimed Inf Retr 6:1–8

    Article  Google Scholar 

  31. Thounaojam DM, Thongam K, Jayshree T, Roy S, Singh KM (2019) Colour histogram and modified multi-layer perceptron neural network based video shot boundary detection. Int Arab J Inf Technol 16:686–693

    Google Scholar 

  32. Tippaya S, Sitjongsataporn S, Tan T, Khan MM, Chamnongthai K (2017) Multi-modal visual features-based video shot boundary detection. IEEE Access 5:12563–12575. https://doi.org/10.1109/ACCESS.2017.2717998

    Article  Google Scholar 

  33. Warhade KK, Merchant SN, Desai UB (2013) Shot boundary detection in the presence of illumination and motion. Signal Image Video Process 7 (3):581–592. https://doi.org/10.1007/s11760-011-0262-4

    Article  Google Scholar 

  34. Yuan Y, Chu J, Leng L, Miao J, Kim BG (2020) A scale-adaptive object-tracking algorithm with occlusion detection. EURASIP J Image Video Process 2020(1):1–15

    Article  Google Scholar 

  35. Zhang Y, Chu J, Leng L, Miao J (2020) Mask-refined r-cnn: a network for refining object details in instance segmentation. Sensors (Basel) 20 (1010):1–16

    Google Scholar 

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Acknowledgements

Sound and Vision video is copyrighted. The Sound and Vision video used in this work is provided solely for research purposes through the TREC Video Information Retrieval Evaluation Project Collection.

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  1. Computer Vision Lab, CSE Department, National Institute of Technology Silchar, Silchar, India

    Saptarshi Chakraborty & Dalton Meitei Thounaojam

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  1. Saptarshi Chakraborty
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  2. Dalton Meitei Thounaojam
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Correspondence to Saptarshi Chakraborty.

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Chakraborty, S., Thounaojam, D.M. SBD-Duo: a dual stage shot boundary detection technique robust to motion and illumination effect. Multimed Tools Appl 80, 3071–3087 (2021). https://doi.org/10.1007/s11042-020-09683-y

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  • DOI: https://doi.org/10.1007/s11042-020-09683-y

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