Journal of Internet Computing and Services (인터넷정보학회논문지)

Korean Society for Internet Information (한국인터넷정보학회)
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Video Summarization Using Importance-based Fuzzy One-Class Support Vector Machine

중요도 기반 퍼지 원 클래스 서포트 벡터 머신을 이용한 비디오 요약 기술

  • 김기주 (한국항공대학교 대학원 컴퓨터공학과) ;
  • 최영식 (한국항공대학교 항공전자 및 정보통신공학부)
  • Received : 2011.03.04
  • Accepted : 2011.07.20
  • Published : 2011.10.31
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Abstract

In this paper, we address a video summarization task as generating both visually salient and semantically important video segments. In order to find salient data points, one can use the OC-SVM (One-class Support Vector Machine), which is well known for novelty detection problems. It is, however, hard to incorporate into the OC-SVM process the importance measure of data points, which is crucial for video summarization. In order to integrate the importance of each point in the OC-SVM process, we propose a fuzzy version of OC-SVM. The Importance-based Fuzzy OC-SVM weights data points according to the importance measure of the video segments and then estimates the support of a distribution of the weighted feature vectors. The estimated support vectors form the descriptive segments that best delineate the underlying video content in terms of the importance and salience of video segments. We demonstrate the performance of our algorithm on several synthesized data sets and different types of videos in order to show the efficacy of the proposed algorithm. Experimental results showed that our approach outperformed the well known traditional method.

본 논문에서는 비디오 요약을 시각적으로 특징이 있고 주관적으로 중요한 비디오 세그먼트 집합을 구하는 새로운 요약 방식을 기술한다. 시각적으로 특징이 있는 데이터 포인트를 찾기 위해 novelty detection으로 잘 알려져 있는 OC-SVM(One-Class Support Vector Machine)을 사용할 수 있다. 그러나 OC-SVM의 처리과정에 비디오 세그먼트에 대한 사용자의 주관적인 중요도를 반영하기는 어렵다. OC-SVM의 처리과정에 사용자의 주관적 중요성을 반영하기 위해서, 본 논문에서는 OC-SVM의 퍼지 버전을 유도한다. IFOC-SVM(Importance-based Fuzzy One-Class Support Vector Machine)은 비디오 세그먼트의 중요도에 따라 각 데이터 포인트에 가중치를 부여하고 데이터 분포의 서포트를 측정한다. 이때, 구해진 서포트 벡터는 비 오 세그먼트의 중요도와 시각적 특징 관점에서 비디오의 내용을 축약하여 표현한다. 제안된 알고리즘의 성능을 증명하기 위하여 가상의 데이터들과 다양한 종류의 비디오들을 가지고 실험하였다. 실험 결과는 제안하는 방법의 성능이 다른 비디오 요약의 성능보다 우수함을 보여주었다.

Keywords

References

  1. Nevenka Dimitrova, et al. "Applications of Video-Content Analysis and Retrieval", IEEE Multimedia, Vol. 9 Issue. 3, Jul-Sep 2002, pp.42-55 https://doi.org/10.1109/MMUL.2002.1022858
  2. Yeung, M.M., Boon-Lock Yeo: Video Visualization for Compact Presentation and Fast Browsing of Pictorial Content. IEEE Transactions on Circuits and Systems for Video Technology. Vol.7 No.5 October (1997) 771-785 https://doi.org/10.1109/76.633496
  3. Shingo Uchihashi, Jonathan Foote, Andreas Girgensohn, John Boreczky: Video Magna: Generating Semantically Meaningful Video Summaries. Proceedings of ACM International Conference on Multimedia (1999) 383--391
  4. Dimitrova, N., Hong-Jiang Zhang, Shahraray, B., Sezan, I.,Huang, T., Zakhor,A.: Applications of Video-Content Analysis and Retrieval. IEEE Multimedia Vol. 9 (2002) 42--55 https://doi.org/10.1109/MMUL.2002.1022858
  5. Girgensohn, A., Boreczky, J., Wilcox, L.: Keyframe-Based User Interfaces for Digital Video. IEEE Computer September (2001) 61--67
  6. Muller, K.-R., Mika, S., Ratsch, G., Tsuda, K., Scholkopf, B.: An Introduction to Kernel-Based Learning Algorithms. IEEE Transactions on Neural Networks Vol.12 No.2 March (2001) 181--202 https://doi.org/10.1109/72.914517
  7. Yunqiang Chen, Xiang Sean Zhou, Huang, T.S.: One-class SVM for learning in image retrieval. Proceedings of International Conference on Image Processing Vol.1 (2001) 34--37.
  8. B. Scholkopf, J.C. Platt, J. Shawe-Taylor, A. J. Smola, R. C. Williamson: Estimating the Support of a High-Dimensional Distribution. Microsoft Research Technical Report MSR-TR-99-87 (1999)
  9. Pei-Yi Hao "Fuzzy one-class support vector machines" Fuzzy Sets and Systems 159 (2008) 2317 -. 2336 https://doi.org/10.1016/j.fss.2008.01.013
  10. 김기주, 최영식 "퍼지 원 클래스 서포트 벡터 머신", 인터넷정보학회논문지 제6권 32호, 2005
  11. YoungSik Choi and KiJoo Kim "Video Summarization Using Fuzzy One-Class Support Vector Machine" LNCS3043, pp.49-/56, 2004
  12. S. W. Smoliar and H. J. Zhang, "Content-based video indexing and retrieval", IEEE Multimedia, 1994, pp. 62-72.
  13. B. Shahraray and D. C. Gibbon, "Automatic generation of pictorial transcripts of video program,s", in Proc. IS&T/SPIE Digital Video Compression: Algorithms and Technologies, SanJose,CA,1995,pp.512-519.
  14. Y. Zhuang, Y. Rui, T. S. Huang, and S. Mehrotra, "Adaptive key frame extraction using unsupervised clustering", ICIP'98, vol. 1, Oct 1998, pp.866-870.
  15. Hanjalic, A., HongJiang Zhang: An Integrated Scheme for Automated Video Abstraction Based on Unsupervised Cluster-Validity Analysis. IEEE Transactions on Circuits and Systems for Video Technology Vol.9 No.8 December (1999) 1280--1289 https://doi.org/10.1109/76.809162
  16. Asa Ben-Hur, David Horn, Hava T. Siegelmann, and Vladimir Vapnik, "Support Vector Clustering", Journal of Machine Learning Research 2, pp.125-137, 2001.
  17. Manuel Davy and simon Godsill, "Detection of abrupt spectral changes using support vector machines: an application to audio signal segmentation", IEEE International Conference on Acoustics, Speech, and signal Processing, Vol. 2, 12-17, pp.1313-1316, May 2002.
  18. P. Hayton, B. Scholkopf, L. Tarassenko, and P. Anuzis, "Support vector novelty detection applied to jet engine vibration spectra", in NIPS'2000, 2000.
  19. Tax, D. M. J. and Duin, R. P. W., "Image database retrieval with Support vector data description", Proceedings of the Sixth Annual Conference of the Advanced School for Computing and Imaging, ASCI, Delft, June 2000.
  20. J. C. Platt "Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines", Microsoft Research Technical Report, MST-TR-98-14, 1998.
  21. Nello Cristianini and John Shawe-Taylor, "An Introduction to Support Vector Machines and other kernel-based learning methos", Cambridge University Press, 2000.
  22. J. M. Keller, et. al., "Evidence Aggregation Networks for Fuzzy Logic Inference", IEEE Transactions on Neural Networks, Vol. 3, No. 5, pp. 762-769, 1992
  23. YoungSik Choi, Sang-Yoon Lee: Scalable Keyframe Extraction Using One-Class Support Vector Machine. ICCS2003 LNCS2660 (2003) 491-499