대한전자공학회논문지SP (Journal of the Institute of Electronics Engineers of Korea SP)

대한전자공학회 (The Institute of Electronics and Information Engineers)
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효율적인 비디오 시퀀스 정합 알고리즘

An Efficient Video Sequence Matching Algorithm

  • 발행 : 2004.09.01
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초록

디지털 미디어의 증가로 비디오 시퀀스를 효율적으로 정합하기 위한 다양한 알고리즘이 제안되었다 기존의 비디오 검색 알고리즘에서는 주로 프레임 단위의 질의에 관한 검색 알고리즘이 연구되었으나 비디오 시퀀스 단위의 질의에 관한 정합 알고리즘 연구는 미진하였다. 본 논문에서는 비디오 시퀀스 질의에 관한 효율적인 비디오 색인과 검색 알고리즘을 제안한다. 시퀀스 정합의 정확도와 성능 향상을 위하여 연속되는 프레임의 히스토그램간의 유사도 함수로 커쉬함수를 사용하였으며 기존의 방법에 비해 높은 성능을 나타내었다. 비디오 샷들로부터 추출된 키프레임들은 샷묶음 뿐만 아니라 비디오 시퀀스 정합이나 브라우징에도 사용되며 여기서 키프레임은 이전 프레임들과 중요한 차이를 보이는 프레임을 나타낸다. 몇가지 키프레임 알고리즘이 제안되었고 적절한 유사도 측정을 통해 샷경계 검출과 유사한 방법으로 키프레임 추출이 가능하다. 본 논문에서는 누적된 커쉬함수를 사용하여 효과적으로 키프레임을 추출하는 알고리즘을 제안하고 기존의 방법들과의 성능을 비교한다. 비디오 시퀀스 정합은 키프레임간의 유사도 측정에 의해 수행될 수 있다 본 논문에서는 추출된 키프레임의 정합 효율을 향상 시키기 위하여 커쉬함수와 하우스도르프 거리를 사용하였다. 몇가지 실험 영상을 이용한 실험결과 제안한 방법은 기존의 방법에 비해적은 계산량으로 높은 정합 성능을 보였다.

According tothe development of digital media technologies various algorithms for video sequence matching have been proposed to match the video sequences efficiently. A large number of video sequence matching methods have focused on frame-wise query, whereas a relatively few algorithms have been presented for video sequence matching or video shot matching. In this paper, we propose an efficientalgorithm to index the video sequences and to retrieve the sequences for video sequence query. To improve the accuracy and performance of video sequence matching, we employ the Cauchy function as a similarity measure between histograms of consecutive frames, which yields a high performance compared with conventional measures. The key frames extracted from segmented video shots can be used not only for video shot clustering but also for video sequence matching or browsing, where the key frame is defined by the frame that is significantly different from the previous fames. Several key frame extraction algorithms have been proposed, in which similar methods used for shot boundary detection were employed with proper similarity measures. In this paper, we propose the efficient algorithm to extract key frames using the cumulative Cauchy function measure and. compare its performance with that of conventional algorithms. Video sequence matching can be performed by evaluating the similarity between data sets of key frames. To improve the matching efficiency with the set of extracted key frames we employ the Cauchy function and the modified Hausdorff distance. Experimental results with several color video sequences show that the proposed method yields the high matching performance and accuracy with a low computational load compared with conventional algorithms.

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참고문헌

  1. V. Kobla, D. Doermann, and K. I. Lin, 'Archiving, indexing, and retrieval of video in compressed domain,' in Proc. SPIE Conf. Multimedia Storage and Archiving Systems, vol. 2916, pp. 78-89, Boston, MA, USA, Nov. 1996 https://doi.org/10.1117/12.257312
  2. B.-L. Yeo and B. Liu, 'Rapid scene analysis on compressed video,' IEEE Trans. Circuits and Systems for Video Technology, vol. CSVT-5, no. 6, pp. 533-544, Dec. 1995 https://doi.org/10.1109/76.475896
  3. M. M. Yeung and B. Liu, 'Efficient matching and clustering of video shots,' in Proc. IEEE Int. Conf. Image Processing, Washington, D. C., USA, Oct. 1995, vol. 1, pp. 338-341 https://doi.org/10.1109/ICIP.1995.529715
  4. Y. S. Avrithis, A. D. Doulamis, N. D. Doulamis, and S. D. Kollias, 'A stochastic framework for optimal key frame extraction from MPEG video databases,' Computer Vision and Image Understanding, vol. 75, no. 1, pp. 3-24, July 1999 https://doi.org/10.1006/cviu.1999.0761
  5. H. S. Chang, S. Sull, and S. U. Lee, 'Efficient video indexing scheme for content-based retrieval,' IEEE Trans. Circuits and Systems for Video Technology, vol. CSVT-9, no. 8, pp. 1269-1279, Dec. 1999 https://doi.org/10.1109/76.809161
  6. F. Dufaux, 'Key frame selection to represent a video,' in Proc. IEEE Int. Conf. Image Processing, Vancouver, Canada, Sep. 2000, vol. 2, pp. 275-278 https://doi.org/10.1109/ICIP.2000.899354
  7. A. Akutsu, Y. Tonomura, H. Hashimoto, and Y. Ohba, 'Video indexing using motion vectors,' in Proc. SPIE Conf. Visual Communications and Image Processing, Boston, MA, USA, Nov. 1992, vol. 1818, pp. 1522-1530 https://doi.org/10.1117/12.131425
  8. S. H. Kim and R.-H. Park, 'A novel approach to video indexing using luminance projection,' in Proc. IASTED Int. Conf. Signal and Image Processing, pp. 359-362, Kauai, HI, USA, Aug. 2002
  9. S. H. Kim and R.-H. Park, 'An efficient algorithm for video sequence matching using the modified Hausdorff distance and the directed divergence,' IEEE Trans. Circuits and Systems for Video Technology, vol. CSVT-12, no. 7, pp. 592-596, July 2002 https://doi.org/10.1109/TCSVT.2002.800512
  10. S. H. Kim and R.-H. Park, 'An efficient algorithm for video sequence matching using the Hausdorff distance and the directed divergence,' in Proc. SPIE Conf. Visual Communications and Image Processing 2001, San Jose, CA, Jan. 2001, vol. 4310, pp. 754-761
  11. N. Sebe, M. S. Lew, and D. P. Huijsmans, 'Toward improved ranking metrics,' IEEE Trans. Pattern Analysis and Machine Intelligence, vol. PAMI-22, no. 10, pp. 1132-1143, Oct. 2000 https://doi.org/10.1109/34.879793
  12. S. H. Kim and R.-H. Park, 'An efficient video sequence matching using the Cauchy function and the modified Hausdorff distance,' in Proc. SPIE Storage and Retrieval for Media Databases 2002, 4676, pp. 232-239, San Jose, CA, USA, Jan. 2002 https://doi.org/10.1117/12.451095
  13. D. A. Adjeroh, M. C. Lee, and I. King, 'A distance measure for video sequences,' Computer Vision and Image Understanding, vol. 75, no. 1, pp. 25-45, July 1999 https://doi.org/10.1006/cviu.1999.0764
  14. S. Santini and R. Jain, 'Similarity measures,' IEEE Trans. Pattern Analysis and Machine Intelligence, vol. PAMI-21. no. 9, pp. 871-883, Sep. 1999 https://doi.org/10.1109/34.790428
  15. D. P. Huttenlocher, G. A. Klanderman, and W. J. Rucklidge, 'Comparing images using the Hausdorff distance,' IEEE Trans. Pattern Analysis and Machine Intelligence, vol. PAMI-15, no. 9, pp. 850-863, Sep. 1993 https://doi.org/10.1109/34.232073
  16. B. S. Manjunath, J.-R. Ohm, V. V. Vasudevan, and A. Yamada, 'Color and texture descriptors,' IEEE Trans. Circuits and Systems for Video Technology, vol. CSVT-11, no. 6, pp. 703-715, June 2001 https://doi.org/10.1109/76.927424