Multimedia Tools and Applications

, Volume 77, Issue 7, pp 8139–8161 | Cite as

Video shot boundary detection using multiscale geometric analysis of nsct and least squares support vector machine

  • Jaydeb Mondal
  • Malay Kumar Kundu
  • Sudeb Das
  • Manish Chowdhury
Article
First Online:
Received:
Revised:
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Abstract

The fundamental step in video content analysis is the temporal segmentation of video stream into shots, which is known as Shot Boundary Detection (SBD). The sudden transition from one shot to another is known as Abrupt Transition (AT), whereas if the transition occurs over several frames, it is called Gradual Transition (GT). A unified framework for the simultaneous detection of both AT and GT have been proposed in this article. The proposed method uses the multiscale geometric analysis of Non-Subsampled Contourlet Transform (NSCT) for feature extraction from the video frames. The dimension of the feature vectors generated using NSCT is reduced through principal component analysis to simultaneously achieve computational efficiency and performance improvement. Finally, cost efficient Least Squares Support Vector Machine (LS-SVM) classifier is used to classify the frames of a given video sequence based on the feature vectors into No-Transition (NT), AT and GT classes. A novel efficient method of training set generation is also proposed which not only reduces the training time but also improves the performance. The performance of the proposed technique is compared with several state-of-the-art SBD methods on TRECVID 2007 and TRECVID 2001 test data. The empirical results show the effectiveness of the proposed algorithm.

Keywords

Shot boundary detection Abrupt transition Gradual transition Principal component analysis Non-subsampled contourlet transform Least squares support vector machine 
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Notes

Acknowledgements

The first author acknowledges Tata Consultancy Services (TCS) for providing fellowship to carry out the research work. Malay K. Kundu acknowledges the Indian National Academy of Engineering (INAE) for their support through INAE Distinguished Professor fellowship. The authors would like to thank the National Institute of Standards & Technology (NIST) for providing TRECVID data set.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Jaydeb Mondal
    • 1
  • Malay Kumar Kundu
    • 1
  • Sudeb Das
    • 2
  • Manish Chowdhury
    • 3
  1. 1.Machine Intelligence UnitIndian Statistical InstituteKolkataIndia
  2. 2.Videonetics Technologies Pvt. Ltd.Salt Lake CityIndia
  3. 3.KTH School of Technology and HealthHuddingeSweden

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