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An Efficient Video Sequence Matching Algorithm  

김상현 (서강대학교 전자공학과)
박래홍 (서강대학교 전자공학과)
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Abstract
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.
Keywords
video sequence matching; key frame extraction; modified Hausdorff distance; Cauchy function; cumulative measure;
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1 S. Santini and R. Jain, 'Similarity measures,' IEEE Trans. Pattern Analysis and Machine Intelligence, vol. PAMI-21. no. 9, pp. 871-883, Sep. 1999   DOI   ScienceOn
2 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   DOI   ScienceOn
3 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   DOI   ScienceOn
4 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
5 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   DOI   ScienceOn
6 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
7 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   DOI   ScienceOn
8 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   DOI
9 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   DOI   ScienceOn
10 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   DOI
11 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   DOI   ScienceOn
12 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   DOI
13 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   DOI
14 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   DOI   ScienceOn
15 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   DOI   ScienceOn
16 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   DOI