Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Shot Group and Representative Shot Frame Detection using Similarity-based Clustering

  • Received : 2016.07.26
  • Accepted : 2016.09.07
  • Published : 2016.09.30
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Abstract

This paper introduces a method for video shot group detection needed for efficient management and summary of video. The proposed method detects shots based on low-level visual properties and performs temporal and spatial clustering based on visual similarity of neighboring shots. Shot groups created from temporal clustering are further clustered into small groups with respect to visual similarity. A set of representative shot frames are selected from each cluster of the smaller groups representing a scene. Shots excluded from temporal clustering are also clustered into groups from which representative shot frames are selected. A number of video clips are collected and applied to the method for accuracy of shot group detection. We achieved 91% of accuracy of the method for shot group detection. The number of representative shot frames is reduced to 1/3 of the total shot frames. The experiment also shows the inverse relationship between accuracy and compression rate.

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References

  1. S. Liu, M. Zhu, Q. Zheng, "Mining similarities for clustering web video clips," International Conference on Computer Science and Software Engineering, pp. 759-762, 2008.
  2. S. Cheung, A. Zakhor, "Fast similarity search and clustering of video sequences on World Wide Web," IEEE Trans on Multimedia, Vol. 7, No. 3, pp.524-537, 2004.
  3. Asghar, M.N., Hussain, F., Manton, R., "Video indexing: a survey," International Journal of Computer and Information Tech. Vol. 3, No. 1, pp. 148-169, 2014.
  4. S.T. Dhagdi, P.R. Deshmukh, "Keyframe based video summarization using automatic threshold & edge matching rate," International Journal of Scientific and Research Publications, Vol. 2, Issue 7, pp.1-12 2012.
  5. J. Baber, N. Afzulpurkar, S. Satoh, "A framework for video segmentation using global and local features," International Journal of Pattern Recognition and AI, Vol. 27, No. 5, Article ID 1355007, pp.1-29, 2013.
  6. X. Zhu, J. Fan, A.K. Elmagarmid, X. Wu "Hierarchical video summarization and content description joint semantic and visual similarity," ACM/Springer Multimedia Systems Journal, Vol. 9, No.1, pp.31-53, 2003. https://doi.org/10.1007/s00530-003-0076-5
  7. A. Vailaya, A.K. Jain, H.J. Zhang, "Video Clustering," Technical report MSUCPS:TR96-64, Michigan State University, 1996.
  8. J. Foo, J. Zobel, R. Sinha, "Clustering Near-Duplicate Images in Large Collections," Proceedings of the International Workshop on Multimedia Information Retrieval, pp.21-30, 2007.
  9. M. Flickner, H. Sawhney, et. al., "Query by image and video content: the QBIC system," Computer Vol. 28, Issue 9, pp.23-32, 1995. https://doi.org/10.1109/2.410146
  10. S.V. Porter, "Video segmentation and indexing using motion estimation," Ph.D. dissertation, U. of Bristol, 2004.
  11. Z. Cernekova, I. Pitas, "Information theory based shot cut/fade detection and video summarization," IEEE Proceedings in circuit and systems for video technology, Vol. 16, No. 1, pp.82-91, 2006
  12. J. Calic, E. Izuierdo, "Efficient key-frame extraction and video analysis," Proceedings of International Conference on IEEE, pp.28-33, 2002
  13. Y. Zhuang, Y. Rui, T.S. Huan, S. Mehrotra, "Adaptive key frame extracting using unsupervised clustering," Proceedings of International Conference on Image Processing, Vol. 1, pp.582-585, 2002
  14. A. Hanjalic, R.L. Lagendijk, J. Biemond, "Automated high level movie segmentation for advance video retrieval systems," IEEE Trans. on Circuits and Systems for Video Technology, Vol. 9, No. 4, pp. 580-588, 1999 https://doi.org/10.1109/76.767124
  15. L.H. Chen, Y.C. Lai, H.Y. Liao, "Movie scene segmentation using background information," Pattern Recognition, Vol. 41, No. 3, pp.1056-1065, 2008. https://doi.org/10.1016/j.patcog.2007.07.024
  16. D-W Chin, T-W Kim, J-M Choi, "Video scene detection using shot clustering based on visual features," Journal of Intelligent Information Systems, Vol. 18, No. 2, pp.47-60, 2012
  17. M. Yeung, B-L Yeo, "Segmentation of video by clustering and graph analysis," Computer Vision and Image Understanding, Vol. 71, No. 1, pp.94-109, 1998 https://doi.org/10.1006/cviu.1997.0628
  18. S-H Kim, D-S Hwang, "A shot change detection algorithm based on frame segmentation and object movement," Journal of the Korea society of computer and information, Vol. 20, No. 5, pp.21-29, 2015 https://doi.org/10.9708/jksci.2015.20.5.021
  19. S-Y Shin, S-B Pyo, "Video abstracting using scene change detection and shot clustering for construction of efficient video database," Journal of the Korea society of computer and information, Vol. 11, No. 2, pp.111-119, 206