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Dual-dissimilarity measure-based statistical video cut detection

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Abstract

Video cut detection is an essential process of temporal continuity-based video applications such as video segmentation, video retargeting, and frame rate up-conversion. The performance of these applications highly depends on the performance of cut detection. This paper proposes an effective and low-complexity approach for detecting video cuts. The proposed method uses two simple dissimilarity measures for video cut detection: inter-frame luminance variation and temporal variation of inter-frame variations over several frames. The first is used to detect abrupt changes, and the second is used to reduce the influence of disturbances, e.g., object or camera motion. The proposed method is comprised of the following three steps. First, it computes the two dissimilarity measures. Then, it combines them using Bayesian estimation and linear regression. Finally, it decides on the possibility of cuts using the combined dissimilarity measure. Experimental results show that the average F1 score of the proposed method was up to 0.252 (37.0%) higher than those of the benchmark methods. Moreover, the algorithmic simplicity of the proposed method reduced the average computation time per pixel by up to 99.8%, when compared with state-of-the-art methods. Thus, the proposed method is superior to existing methods in terms of computational complexity and detection accuracy.

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References

  1. Guan, G., Wang, Z., Lu, S., Deng, J.D.: Keypoint based keyframe selection. IEEE Trans. Circuits Syst. Video Technol. 23(4), 729–734 (2013)

    Article  Google Scholar 

  2. Hampapur, A., Weymouth, T., Jain, R.: Digital video segmentation.In: Proceedings ACM Multimedia, pp. 357–364, (1994)

  3. Tarabalka, Y., Charpiat, G., Brucker, L., Menze, B.H.: Spatio-temporal video segmentation with shape growth or shrinkage constraint. IEEE Trans. Image Process. 23(9), 3829–3840 (2014)

    Article  MathSciNet  Google Scholar 

  4. Li, C., Lin, L., Zuo, W., Yan, S., Tang, J.:Sold: sub-optimal low-rank decomposition for efficient video segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5519–5527, (2015)

  5. Tambo, A.L., Bhanu, B.: Segmentation of pollen tube growth videos using dynamic bi-modal fusion and seam carving. IEEE Trans. Image Process. 25(5), 1993–2004 (2016)

    Article  Google Scholar 

  6. Avidan, S., Shamir, A.: Seam carving for content-aware image resizing. ACM Trans. Graphics 26(3), 10 (2007)

    Article  Google Scholar 

  7. Krahenbuhl, P., Lang, M., Hornung, A., Markus, G.: A system for retargeting of streaming video. ACM Trans. Graphics 28(5), 126 (2009)

    Article  Google Scholar 

  8. Cheng, W.-H., Wang, C.-W., Wu, J.-L.: Video adaptation for small display based on content recomposition. IEEE Trans. Circuits Syst. Video Technol. 17(1), 43–58 (2007)

    Article  Google Scholar 

  9. Patti, A.J., Sezan, M.I., Tekalp, A.M.: High resolution standards conversion of low resolution video. In: Proceedings IEEE International Conference Acoustics, Speech, Signal Processing, vol. 4, pp. 2197–2200, (1995)

  10. Kang, S.-J., Cho, K.R., Kim, Y.H.: Motion compensated frame rate up-conversion using extended bilateral motion estimation. IEEE Trans. Consumer Electron. 53(4), 1759–1767 (2007)

    Article  Google Scholar 

  11. Kang, S.-J., Yoo, S., Kim, Y.H.: Dual motion estimation for frame rate up-conversion. IEEE Trans. Circuits Syst. Video Technol. 20(12), 1909–1914 (2010)

    Article  Google Scholar 

  12. Kang, S.-J.: Adaptive luminance coding-based scene-change detection for frame rate up-conversion. IEEE Trans. Consumer Electron. 59(2), 370–375 (2013)

    Article  Google Scholar 

  13. Zhang, H., Kankanhalli, A., Smoliar, S.: Automatic partitioning of full-motion video. Multimedia Syst. 1(1), 10–28 (1993)

    Article  Google Scholar 

  14. Yeo, B.L., Liu, B.: Rapid scene analysis on compressed video. IEEE Trans. Circuits Syst. Video Technol. 5(6), 90–105 (1995)

    Google Scholar 

  15. Hanjalic, A.: Shot-boundary detection: unraveled and resolved? IEEE Trans. Circuits Syst. Video Technol. 12(2), 90–105 (2002)

    Article  Google Scholar 

  16. Ma, Y.F., Sheng, J., Chen, Y., Zhang, H.J.: MSR-ASIA at TREC-10 video track: shot boundary detection task. In: Proceedings 10th Text Retrieval Conference (TREC), pp. 371–377, (2001)

  17. Dimou, A., Nemethova, O., Rupp, M.: Scene change detection for H. 264 using dynamic threshold techniques. In: Proceedings 5th EURASIP Conference Speech Image Process. Multimedia Common. Service, pp. 1–6, (2005)

  18. Kim, J.R., Suh, S., Sull, S.: Fast scene change detection for personal video recorder. IEEE Trans. Consumer Electron. 49(3), 683–688 (2003)

    Article  Google Scholar 

  19. Tan, Y.P., Nagamani, J., Lu, H.: Modified Kolmogorov-Smirnov metric for shot boundary detection. Electron. Lett. 39(18), 1313–1315 (2003)

    Article  Google Scholar 

  20. Chasanis, V., Lias, A., Galatsanos, N.: Simultaneous detection of abrupt cuts and dissolves in videos using support vector machines. Pattern Recogn. Lett. 30(1), 55–65 (2009)

    Article  Google Scholar 

  21. Bendraou, V., Essannouni, F., Aboutajdine, D., Salam, A.: Video shot boundary detection method using histogram differences and local image descriptor. In: Complex Systems (WCCS), 2014 Second World Conference on, (2014)

  22. Shu, H., Chau, L.-P.: A new scene change feature for video transcoding. In: Proceedings IEEE International Symposium on Circuits and Systems, pp. 4582–4585, (2005)

  23. Lin, W., Sun, M.T., Li, H., Hu, H.M.: A new shot change detection method using information from motion estimation. In: Pacific-Rim Conference on Multimedia, pp. 264–275, (2010)

    Chapter  Google Scholar 

  24. Lee, J., Kim, S.-J., Lee, C.S.: Effective scene change detection by using statistical analysis of optical flows. Appl. Math Info. Sci. 6(1), 177–183 (2012)

    Google Scholar 

  25. Yuan, J., Wang, H., Xiao, L., Zheng, W., Li, J., Lin, F., Zhang, B.: A formal study of shot boundary detection. IEEE Trans. Circuits Syst. Video Technol. 17(2), 168–186 (2007)

    Article  Google Scholar 

  26. Kucuktunc, O., Gudukbay, U., Ulusoy, O.: Fuzzy color histogram-based video segmentation. Comput. Vis. Image Underst. 114(1), 125–134 (2010)

    Article  Google Scholar 

  27. Kang, S.-J., Cho, S.I., Yoo, S., Kim, Y.H.: Scene change detection using multiple histograms for motion-compensated frame rate up- conversion. J. Display Technol. 8(3), 121–126 (2012)

    Article  Google Scholar 

  28. (2001). TREC video retrieval test collection (Online). http://www.open-video.org/collection_detail.php?cid=7

  29. Sun, J., Wan, Y.: A novel metric for efficient video shot boundary detection. In: Proceedings 2014 International Conference on Visual Communication and Image Processing, pp. 45–48, (2014)

  30. Yang, Y., Liu, X.: A re-examination of text categorization methods. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 42–49, (1999)

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Acknowledgements

This research was supported by LG Display Co., Ltd., IDEC, and the MSIP Ministry of Science, ICT and Future Planning), Korea, under the “ICT Consilience Creative Program” (IITP-R0346-16-1007) supervised by the ITTP (Institute for Information and communications Technology Promotion).

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Authors and Affiliations

  1. Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, Korea

    Gyujin Bae & Young Hwan Kim

  2. Department of Electronic Engineering, College of Information and Communication Engineering, Daegu University, Gyeongbuk, 38453, Korea

    Sung In Cho

  3. Department of Electronic Engineering, Sogang University, Seoul, 121-742, Korea

    Suk-Ju Kang

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  1. Gyujin Bae
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  2. Sung In Cho
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  3. Suk-Ju Kang
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  4. Young Hwan Kim
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Correspondence to Young Hwan Kim.

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Bae, G., Cho, S.I., Kang, SJ. et al. Dual-dissimilarity measure-based statistical video cut detection. J Real-Time Image Proc 16, 1987–1997 (2019). https://doi.org/10.1007/s11554-017-0696-1

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  • DOI: https://doi.org/10.1007/s11554-017-0696-1

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