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An experimental comparative study on slide change detection in lecture videos

  • Purushotham Eruvaram
  • Kasarapu Ramani
  • C. Shoba Bindu
Original Research
  • 1 Downloads

Abstract

In today’s world e-learning is one of the popular modes of learning and video lectures are more prominent in keeping learners engaged with course. Internet enabled to keep a large number of video lectures on-line. To search for a required topic or subtopic from this huge video repository is becoming very tedious. One way to search for a particular topic is through keyword based search and it is based on extraction of text content available in lecture video files and to achieve it one has to maintain metadata. To maintain the metadata associated with video the frames of video containing text are required to be processed. As the video contains multiple frames per second it is not required to consider each and every frame. So, the frames containing distinct content called key frames need to be identified. The identification of key frames plays crucial role in the lecture video searching process. In this paper, different techniques for key frame identification are experimentally tested and results were compared.

Keywords

Video lecture Key frame Color Histogram Black Pixel Distribution Difference Precision Recall Slide change detection 

Notes

Acknowledgements

This work is supported by University Grants Commission (UGC) under Minor Research Project titled “Fast Content Based Search, Navigation and Retrieval system for E-Learning”. Project Id: F.No:4-4/2015(MRP/UGC-SERO).

References

  1. 1.
    Yamin M, Aljehani SA (2016) E-learning and women in Saudi Arabia: an empirical study. BVICAM’s Int J Inf Technol 8(1):950–954Google Scholar
  2. 2.
    Daga BS, Thakare VM (2017) Semantic enriched lecture video retrieval system using feature mixture and hybrid classification. Adv Image Video Process 5(3):01Google Scholar
  3. 3.
    Li K et al (2015) Structuring lecture videos by automatic projection screen localization and analysis. IEEE Trans Pattern Anal Mach Intell 37(6):1233–1246CrossRefGoogle Scholar
  4. 4.
    Ma D, Xie B, Agam G (2014) A machine learning based lecture video segmentation and indexing algorithm. In: Document recognition and retrieval XXI, vol 9021. International Society for Optics and Photonics, p 90210VGoogle Scholar
  5. 5.
    Masneri S, Schreer O (2014) SVM-based video segmentation and annotation of lectures and conferences. In: Computer vision theory and applications (VISAPP), 2014 international conference on, vol 2. IEEEGoogle Scholar
  6. 6.
    Adcock J et al (2010) Talkminer: a lecture webcast search engine. In: Proceedings of the 18th ACM international conference on multimedia. ACMGoogle Scholar
  7. 7.
    Sandesh BJ et al (2017) Lecture video indexing and retrieval using topic keywords. World Acad Sci Eng Technol Int J Comput Electr Autom Control Inf Eng 11(9):1007–1011Google Scholar
  8. 8.
    Repp S, Grob A, Meinel C (2008) Browsing within lecture videos based on the chain index of speech transcription. IEEE Trans Learn Technol 1(3):145–156CrossRefGoogle Scholar
  9. 9.
    Radha N (2016) Video retrieval using speech and text in video. In: Inventive computation technologies (ICICT), international conference on, vol 2. IEEEGoogle Scholar
  10. 10.
    Yang H, Meinel C (2014) Content based lecture video retrieval using speech and video text information. IEEE Trans Learn Technol 7(2):142–154CrossRefGoogle Scholar
  11. 11.
    Kamabathula VK, Iyer S (2011) Automated tagging to enable fine-grained browsing of lecture videos. In: Technology for education (T4E), 2011 IEEE international conference on. IEEEGoogle Scholar
  12. 12.
    Haubold A, Kender JR (2005) Augmented segmentation and visualization for presentation videos. In: Proceedings of the 13th annual ACM international conference on multimedia. ACMGoogle Scholar
  13. 13.
    Hu W et al (2011) A survey on visual content-based video indexing and retrieval. IEEE Trans Syst Man Cybern Part C (Appl Rev) 41(6):797–819CrossRefGoogle Scholar
  14. 14.
    Jeong HJ et al (2015) Automatic detection of slide transitions in lecture videos. Multimed Tools Appl 74(18):7537–7554CrossRefGoogle Scholar
  15. 15.
    Wang X, Kankanhalli M (2009) Robust alignment of presentation videos with slides. In: Proceedings of the 10th pacific rim conference on multimedia, PCM’09. Springer, Berlin, pp 311–322Google Scholar
  16. 16.
    De Lucia A et al (2008) Migrating legacy video lectures to multimedia learning objects. Softw Pract Exp 38(14):1499CrossRefGoogle Scholar

Copyright information

© Bharati Vidyapeeth's Institute of Computer Applications and Management 2018

Authors and Affiliations

  1. 1.Department of CSEJNTUKKakinadaIndia
  2. 2.Department of ITSree Vidyanikethan Engineering CollegeTirupatiIndia
  3. 3.Department of CSEJNTUA College of EngineeringAnanthapuramuIndia

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