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A Novel Approach for Shot Boundary Detection in Videos

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Multimedia Processing, Communication and Computing Applications

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 213))

Abstract

This paper presents a novel approach for video shot boundary detection. The proposed approach is based on split and merge concept. A fisher linear discriminant criterion is used to guide the process of both splitting and merging. For the purpose of capturing the between class and within class scatter we employ 2D2 FLD method which works on texture feature of regions in each frame of a video. Further to reduce the complexity of the process we propose to employ spectral clustering to group related regions together to a single there by achieving reduction in dimension. The proposed method is experimentally also validated on a cricket video. It is revealed that shots obtained by the proposed approach are highly cohesive and loosely coupled.

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References

  1. Idris F, Panchanathan S (1997) Review of image and video indexing techniques. J Vis Commun Image Represent 8(2):146–166

    Article  Google Scholar 

  2. Zhang D, Lu G (2001) Segmentation of moving objects in image sequence: a review. Circuits Syst Signal Process 2(2):143–183

    Google Scholar 

  3. Koprinska I, Carrato S (2001) Temporal video segmentation: a survey signal processing. IEEE Trans 16(5):413–506

    Google Scholar 

  4. Manjunath S, Guru DS, Suraj MG, Harish BS (2011) A non parametric shot boundary detection: an Eigen gap based approach. Proceedings of fourth annual ACM Bangalore conference, vol 1. pp 1030–1036

    Google Scholar 

  5. Yasira Beevi CP, Natarajan Dr S (2009) An efficient video segmentation algorithm with real time adaptive threshold technique. Int J Sig Process Image Process Pattern Recognition 2(4):13–28

    Google Scholar 

  6. Reddy PVN, Satya Prasad K (2011) Color and texture features for content based image retrieval. Int J Comp Tech Appl 2(4):1016–1020

    Google Scholar 

  7. Quynh NH, Ha NTT, Tao NQ (2012) An efficient content based image retrieval method for retrieving images. Int J Innovative Comput Inf Control 8(4):2823–2836

    Google Scholar 

  8. Yi T, William I (1997) Object-based image retrieval using point feature maps. Proceedings of the International Conference on Database Semantics (DS-8), Rotorua, pp 59–73

    Google Scholar 

  9. Wang H, Divakaran A, Vetro A, Chang SF, Sun H (2003) Survey of compressed-domain features used in audio-visual indexing and analysis. J Visual Commun Image Represent 14:150–183

    Article  Google Scholar 

  10. Patel BV, Meshram Shah BB (2012) Anchor Kutchhi Polytechnic, Content based video retrieval systems. Int J Ubi Comp (IJU) 3(2):13–30

    Google Scholar 

  11. Liua Y, Zhanga D, Lua G, Mab WY (2007) A survey of content-based image retrieval with high-level semantics. Pattern Recogn 40:262–282

    Article  Google Scholar 

  12. Fu X, Xian Zeng J (2009) An effective video shot boundary detection method based on the local color features of interest points. In Proceedings of the 2009 second international symposium on electronic commerce and security, pp 25–28

    Google Scholar 

  13. Don Adjeroh MC, Lee NB, Uma K (2009) Adaptive edge-oriented shot boundary detection. EURASIP J Image Video Process, Hindawi Publishing Corporation. doi:10.1155/2009/859371

  14. Yuchou C, Lee DJ, Yi H, James A (2008) Unsupervised video shot detection using clustering ensemble with a color global scale-invariant feature transform descriptor. J Image Video Proc 1:1–10

    Google Scholar 

  15. Brunelli R, Mich O, Modena CM (1999) A survey on the automatic indexing of video data. J Vis Commun Image Represent 10:78–112

    Article  Google Scholar 

  16. Koumaras H, Gardikis G, Xilouris G, Pallis E, Kourtisa A (2005) Shot boundary detection without threshold parameters. Paper 05210LRR 36(2):133–144

    Google Scholar 

  17. Boreczky JS, Rowe LA (1996) Comparison of video shot boundary detection techniques. J Electron Imaging 5(2):122–128

    Article  Google Scholar 

  18. Alan FS, Palu O, Aiden RD (2010) Video shot boundary detection: Seven years of TRECVid activity. Comput Vis Image Und 114(4):411–418

    Google Scholar 

  19. Abdelati MA, Ben AA, Mtibaa A (2010) Video shot boundary detection using motion activity descriptor. J Telecommun 2(1):54–59

    Google Scholar 

  20. Yao N (2002) Student member, adaptive rood pattern search for fast block-matching motion estimation. IEEE Trans Image Process 11(12):1442–1449

    Google Scholar 

  21. Chen W, Zhang Y-J (2008) Parametric model for video content analysis. Pattern Recogn 29:181–191

    Article  MATH  Google Scholar 

  22. Jacobs A, Miene A, Ioannidis GT, Herzog O (2004) Automatic shot boundary detection combining color, edge, and motion features of adjacent frames, TRECVID 2004 Workshop Notebook Papers, National Institute of Standards and Technology, Gaithersburg, pp 197–206

    Google Scholar 

  23. Lef Evre S Holler J, Vincent N (2003) A review of real-time segmentation of uncompressed video sequences for content-based search and retrieval. Real-Time Imaging 9(1):73–98

    Google Scholar 

  24. Uros Damnjanovic (2007) Ebroul Izquierdo and Marcin Grzegorzek, Shot boundary detection using spectral clustering 15th European signal processing conference

    Google Scholar 

  25. Anuj G, Punitha P, Frank H, Joemon MJ (2009) Split and merge based story segmentation in news videos. Adv Inf Retrieval 5478:766–770

    Google Scholar 

  26. Robuet MH (1973) Shanmugam and its hak dinstein, texture features for image classification. IEEE Trans Man Cybern 3(6):610–621

    Google Scholar 

  27. Marco Barreno (2004) Spectral Methods for Image Clustering, CS 281b: Advanced topics in learning and decision-makinghttp://marcobarreno.com/classes/projects/cs281b/

  28. Ulrike von Luxburg (2007) A tutorial on spectral clustering. Stat Comput 17(4):395–416

    Google Scholar 

  29. Denis Hamad and Philippe Biela, Introduction to spectral clustering

    Google Scholar 

  30. MaxWelling, Fisher Linear Discriminant Analysis. Max welling’s classnotes in machine learning 16(7):817–830http://www.ics.uci.edu/~welling/classnotes/classnotes.html

  31. Nagabhushana P, Guru DS, Shekara BH (2006) (2D)2 FLD: An efficient approach for appearance based object recognition. Neurocomputing 69:934–940

    Article  Google Scholar 

  32. Huilin X, Swamy MNS, Ahmad MO (2005) Two-dimensional FLD for face recognition. Pattern Recogn 38:1121–1124

    Google Scholar 

  33. Jordan MI, Andrew Y Ng, Weiss Y (2002) On spectral clustering: analysis and an algorithm. Advances in neural information processing systems, vol 14. MIT Press, Cambridge, pp 849–856

    Google Scholar 

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Authors and Affiliations

  1. Department of Studies in Computer Science, University of Mysore, Manasagangothri, Mysore, India

    D. S. Guru, Mahamad Suhil & P. Lolika

Authors
  1. D. S. Guru
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  2. Mahamad Suhil
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  3. P. Lolika
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Corresponding author

Correspondence to D. S. Guru .

Editor information

Editors and Affiliations

  1. Master of Computer Applications, PES Institute of Technology, Banashankari 3rd stage, Near Hoskerehalli Cross 100 Feet, Bangalore, 560085, Karnataka, India

    Punitha P. Swamy

  2. Studies in Computer Science, University of Mysore, Manasagangotri, Mysore, 570006, Karnataka, India

    Devanur S. Guru

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© 2013 Springer India

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Guru, D.S., Suhil, M., Lolika, P. (2013). A Novel Approach for Shot Boundary Detection in Videos. In: Swamy, P., Guru, D. (eds) Multimedia Processing, Communication and Computing Applications. Lecture Notes in Electrical Engineering, vol 213. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1143-3_17

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  • DOI: https://doi.org/10.1007/978-81-322-1143-3_17

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-1142-6

  • Online ISBN: 978-81-322-1143-3

  • eBook Packages: EngineeringEngineering (R0)

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