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Video shot boundary detection using block based cumulative approach

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Abstract

Video data is becoming an indispensable part of today’s Big Data due to evolution of social web and mobile technology. Content based video analysis has become crucial for video management. Shot boundary detection is one of the most essential task in video content analysis. In view of this, an efficient shot boundary detection approach to detect abrupt and gradual transition in videos is proposed in this work. The approach extracts block based Mean Cumulative Sum Histogram (MCSH) from each edge gradient fuzzified frame as a combination of local and global feature. The relative standard deviation (RSD) statistical measure is applied on the obtained MCSH to detect abrupt and gradual shots in the video. Efficacy of the proposed method is measured by conducting experiments on TRECVID 2001, TRECVID 2007 and VideoSeg datasets. The proposed method shows relatively a good performance when compared to some of the state-of-the-art shot boundary detection approaches.

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References

  1. Abdesselam A (2013) Improving local binary patterns techniques by using edge information. Lecture Notes on Software Engineering 1(4):360

    Article  Google Scholar 

  2. Abdulhussain SH, Mahmmod BM, Saripan MI, Al-Haddad SAR, Jassim WA (2019) Shot boundary detection based on orthogonal polynomial. Multimed Tools Appl 78(14):20361–20382

    Article  Google Scholar 

  3. 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):214

    Article  Google Scholar 

  4. Adjeroh D, Lee MC, Banda N, Kandaswamy U (2009) Adaptive edge-oriented shot boundary detection. EURASIP Journal on Image and Video Processing 2009(1):859371

    Google Scholar 

  5. Alshennawy AA, Aly AA (2009) Edge detection in digital images using fuzzy logic technique. World Acad Sci Eng Technol 51:178–186

    Google Scholar 

  6. Bezdek JC, Chandrasekhar R, Attikouzel Y (1998) A geometric approach to edge detection. IEEE Trans Fuzzy Syst 6(1):52–75

    Article  Google Scholar 

  7. Bhaumik H, Bhattacharyya S, Nath MD, Chakraborty S (2016) Hybrid soft computing approaches to content based video retrieval: A brief review. Appl Soft Comput 46:1008–1029

    Article  Google Scholar 

  8. Bhaumik H, Chakraborty M, Bhattacharyya S, Chakraborty S (2017). Detection of Gradual Transition in Videos: Approaches and Applications. In Intelligent Analysis of Multimedia Information (pp. 282-318). IGI Global

  9. Camarena JG, Gregori V, Morillas S, Sapena A (2010) Two-step fuzzy logic-based method for impulse noise detection in colour images. Pattern Recogn Lett 31(13):1842–1849

    Article  Google Scholar 

  10. Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 6:679–698

    Article  Google Scholar 

  11. Cirne MVM, Pedrini H (2018) VISCOM: A robust video summarization approach using color co-occurrence matrices. Multimed Tools Appl 77(1):857–875

    Article  Google Scholar 

  12. Dadashi R, Kanan HR (2013) AVCD-FRA: A novel solution to automatic video cut detection using fuzzy-rule-based approach. Comput Vis Image Underst 117(7):807–817

    Article  Google Scholar 

  13. Dimitrova N, Zhang HJ, Shahraray B, Sezan I, Huang T, Zakhor A (2002) Applications of video-content analysis and retrieval. IEEE multimedia 3:42–55

    Article  Google Scholar 

  14. Ford RM, Robson C, Temple D, Gerlach M (2000) Metrics for shot boundary detection in digital video sequences. Multimedia Systems 8(1):37–46

    Article  Google Scholar 

  15. Gygli M (2018). Ridiculously fast shot boundary detection with fully convolutional neural networks. In 2018 International Conference on Content-Based Multimedia Indexing (CBMI) (pp. 1–4). IEEE

  16. Hannane R, Elboushaki A, Afdel K, Naghabhushan P, Javed M (2016) An efficient method for video shot boundary detection and keyframe extraction using SIFT-point distribution histogram. International Journal of Multimedia Information Retrieval 5(2):89–104

    Article  Google Scholar 

  17. Hassanien A, Elgharib M, Selim A, Bae S H, Hefeeda M, Matusik W (2017). Large-scale, fast and accurate shot boundary detection through spatio-temporal convolutional neural networks. arXiv preprint arXiv:1705.03281

  18. 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 

  19. Hu W, Xie N, Li L, Zeng X, Maybank S (2011) A survey on visual content-based video indexing and retrieval. IEEE Trans Syst Man Cybern Part C Appl Rev 41(6):797–819

    Article  Google Scholar 

  20. Jadon RS, Chaudhury S, Biswas KK (2001) A fuzzy theoretic approach for video segmentation using syntactic features. Pattern Recogn Lett 22(13):1359–1369

    Article  MATH  Google Scholar 

  21. Jain AK, Vailaya A, Wei X (1999) Query by video clip. Multimedia Systems 7(5):369–384

    Article  Google Scholar 

  22. Ji Q G, Feng J W, Zhao J, Lu Z M (2010). Effective dissolve detection based on accumulating histogram difference and the support point. In 2010 First International Conference on Pervasive Computing, Signal Processing and Applications (pp. 273-276). IEEE

  23. Jiang X, Sun T, Liu J, Chao J, Zhang W (2013) An adaptive video shot segmentation scheme based on dual-detection model. Neurocomputing 116:102–111

    Article  Google Scholar 

  24. Küçüktunç O, Güdükbay U, Ulusoy Ö (2010) Fuzzy color histogram-based video segmentation. Comput Vis Image Underst 114(1):125–134

    Article  Google Scholar 

  25. Lee MS, Yang YM, Lee SW (2001) Automatic video parsing using shot boundary detection and camera operation analysis. Pattern Recogn 34(3):711–719

    Article  MATH  Google Scholar 

  26. Lee MH, Yoo HW, Jang DS (2006) Video scene change detection using neural network: Improved ART2. Expert Syst Appl 31(1):13–25

    Article  Google Scholar 

  27. Li Z, Liu X, Zhang S (2016). Shot Boundary Detection based on Multilevel Difference of Colour Histograms. In 2016 First International Conference on Multimedia and Image Processing (ICMIP) (pp. 15-22). IEEE

  28. Lian S (2011) Automatic video temporal segmentation based on multiple features. Soft Comput 15(3):469–482

    Article  Google Scholar 

  29. Lopez-Molina C, De Baets B, Bustince H (2011) Generating fuzzy edge images from gradient magnitudes. Comput Vis Image Underst 115(11):1571–1580

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  31. Mahmoud M S(2017). Fuzzy Control, Estimation and Diagnosis: Single and Interconnected Systems. Springer.

  32. Mas J, Fernandez G (2003). Video shot boundary detection based on color histogram. Notebook Papers TRECVID2003, Gaithersburg, Maryland, NIST, 15.

  33. Melin P, Mendoza O, Castillo O (2010) An improved method for edge detection based on interval type-2 fuzzy logic. Expert Syst Appl 37(12):8527–8535

    Article  Google Scholar 

  34. Pal SK, King RA (1983) On edge detection of X-ray images using fuzzy sets. IEEE Trans Pattern Anal Mach Intell 1:69–77

    Article  Google Scholar 

  35. Perez-Ornelas F, Mendoza O, Melin P, Castro JR, Rodriguez-Diaz A, Castillo O (2015) Fuzzy index to evaluate edge detection in digital images. PLoS One 10(6):e0131161

    Article  Google Scholar 

  36. Prasertsakul P, Kondo T, Iida, H, Phatrapornnant (2020) Camera operation estimation from video shot using 2D motion vector histogram. Multimed Tools Appl 1–24

  37. Prewitt J M S (1970). Object enhancement and extraction picture processing and psychopictorics

  38. Priya GL, Domnic S (2012) Edge strength extraction using orthogonal vectors for shot boundary detection. Procedia Technology 6:247–254

    Article  Google Scholar 

  39. Rashmi B S, Nagendraswamy H S (2016). Abrupt Shot Detection in Video using Weighted Edge Information. In Proceedings of the International Conference on Informatics and Analytics (p. 69). ACM.

  40. Rashmi B S, Nagendraswamy H S (2016). Video shot boundary detection using midrange local binary pattern. In Advances in Computing, Communications and Informatics (ICACCI), 2016 International Conference on (pp. 201-206). IEEE

  41. Rashmi BS, Nagendraswamy HS (2018) Effective Video Shot Boundary Detection and Keyframe Selection using Soft Computing Techniques. International Journal of Computer Vision and Image Processing (IJCVIP) 8(2):27–48

    Article  Google Scholar 

  42. Sasithradevi A, Roomi SMM (2020) A new pyramidal opponent color-shape model based video shot boundary detection. J Vis Commun Image Represent 67:102754

    Article  Google Scholar 

  43. Shahraray B (1995) Scene change detection and content-based sampling of video sequences. In Digital Video Compression: Algorithms and Technologies 1995 (Vol. 2419, pp. 2-14). International Society for Optics and Photonics

  44. Shen J, Tao D, Li X (2008) Modality mixture projections for semantic video event detection. IEEE Transactions on Circuits and Systems for Video Technology 18(11):1587–1596

    Article  Google Scholar 

  45. Sobel I, Feldman G (1968). A 3x3 isotropic gradient operator for image processing. a talk at the Stanford Artificial Project in 271-272

  46. Stanchev P, Green D Jr, Dimitrov B (2003) High level color similarity retrieval. International Journal of Information Theories and Applications 10(3):363–369

    Google Scholar 

  47. Tab F A, Shahryari O K (2009). Fuzzy edge detection based on pixel's gradient and standard deviation values In Computer Science and Information Technology, 2009. IMCSIT'09. International Multiconference on (pp. 7-10). IEEE

  48. Tao D (Ed.) (2009). Semantic mining technologies for multimedia databases. IGI Global.

  49. Thounaojam DM, Bhadouria VS, Roy S, Singh KM (2017) Shot boundary detection using perceptual and semantic information. International Journal of Multimedia Information Retrieval 6(2):167–174

    Article  Google Scholar 

  50. Thounaojam DM, Khelchandra T, Singh KM, Roy S (2016) A genetic algorithm and fuzzy logic approach for video shot boundary detection. Computational intelligence and neuroscience 2016:14

    Article  Google Scholar 

  51. Tizhoosh H R (2002). Fast fuzzy edge detection. In Fuzzy Information Processing Society, 2002. Proceedings. NAFIPS. 2002 Annual Meeting of the North American IEEE 239-242

  52. Torre V, Poggio TA (1986) On edge detection. IEEE Trans Pattern Anal Mach Intell 2:147–163

    Article  Google Scholar 

  53. VideoSeg n.d.. http://www.site.uottawa.ca/~laganier/videoseg/

  54. Wu G, Liu L, Guo Y, Ding G, Han J, Shen J, Shao L (2017). Unsupervised deep video hashing with balanced rotation. IJCAI

  55. Wu B, Xu L (2014) Integrating bottom-up and top-down visual stimulus for saliency detection in news video. Multimed Tools Appl 73(3):1053–1075

    Article  Google Scholar 

  56. Yuan J, Wang H, Xiao L, Zheng W, Li J, Lin F, Zhang B (2007) A formal study of shot boundary detection. IEEE transactions on circuits and systems for video technology 17(2):168–186

    Article  Google Scholar 

  57. Zadeh LA (1965) Information and control. Fuzzy sets 8(3):338–353

    Google Scholar 

  58. Zhang D, Lei W, Zhang W, Chen X (2019) Shot boundary detection based on block-wise principal component analysis. Journal of Electronic Imaging 28(2):023029

    Google Scholar 

  59. Zhang D, Qi W, Zhang H J (2001). A new shot boundary detection algorithm. In Pacific-Rim Conference on Multimedia (pp. 63-70). Springer, Berlin, Heidelberg

  60. Zheng J, Zou F, Shi M (2004). An efficient algorithm for video shot boundary detection. In Intelligent Multimedia, Video and Speech Processing, 2004. Proceedings of 2004 International Symposium on IEEE 266-269

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Acknowledgments

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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Authors and Affiliations

  1. DoS in Computer Science, University of Mysore, Mysore, 570006, India

    B. S. Rashmi & H. S. Nagendraswamy

  2. Department of Information Technology, Karnataka State Open University, Mysore, 570006, India

    B. S. Rashmi

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Correspondence to B. S. Rashmi.

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Rashmi, B.S., Nagendraswamy, H.S. Video shot boundary detection using block based cumulative approach. Multimed Tools Appl 80, 641–664 (2021). https://doi.org/10.1007/s11042-020-09697-6

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