Abstract
In this paper we present a machine learning system that can accurately predict the transitions between frames in a video sequence. We propose a set of novel features and describe how to use dominant features based on a coarse-to-fine strategy to accurately predict video transitions.
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References
Alattar, A.M.: Detecting fade regions in uncompressed video sequences. In: Proc. IEEE. ICASSP 1997, pp. 3025–3028 (1997)
Boccignone, G., de Santo, M., Percanella, G.: An algorithm for video cut detection in Mpeg sequences. In: Proc. SPIE, Storage and Retrieval for Media Databases, San Jose, CA (2000)
Boresczky, S., Rowe, L.A.: A comparison of video shot boundary detection techniques. Proc. SPIE 2664, 170–179 (1996)
Boreczky, J.S., Wilcox, L.D.: A Hidden Markov Model framework for video segmentation using audio and image features. In: Proceedings of ICASSP 1998, Seattle, May 1998, pp. 3741–3744 (1998)
Brunelli, R., Mich, O., Modena, C.M.: A survey on video indexing, IRST-Technical report 9612-06 (1996)
Dailianas, A., Allen, R.B., England, P.: Comparison of automatic video segmentation algorithms. Proc. SPIE Photonics West 2615, 2–16 (1995)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification. Wiley, Chichester (2001)
Fernando, W.A.C., Canagarajah, C.N., Bull, D.R.: Fade and dissolve detection in uncompressed and compressed video sequence. In: Proc. ICIP Conference, pp. 299–303 (1999)
Gargi, U., Kasturi, R., Antani, S.: Performance characterization and comparison of video indexing algorithms. In: Proc. IEEE CVPR, pp. 559–565 (1998)
Jolion, J.M.: Feature similarity. In: Lew, M.S. (ed.) Principles of Visual Information Retrieval, Springer, Heidelberg (2001)
Kobla, V., Dementhon, D., Doermann, D.: Special effect edit detection using Video-Trails: a comparison with existing techniques. In: Proc. SPIE, pp. 302–310 (1999)
Koprinska, Carrato, S.: Video segmentation- a survey. Signal Processing: Image Communication 16(5), 477–500 (2001)
Lienhart, R.: Comparison of automatic shot boundary detection algorithms. In: Proceedings of SPIE, pp. 3656–3659 (1999)
Lienhart, R.: Reliable Transition Detection in Videos: A survey and practitioner’s guide. International Journal of Image and Graphics 1, 469–486 (2001)
Lienhart, R., Zaccarin, A.: A system for reliable dissolve detection in videos. In: Proc. IEEE ICIP Conference, Thessaloniki (2001)
Meng, J., Juan, Y., Chang, S.F.: Scene change detection in a MPEG compressed video sequence. In: Proc. IS&T/SPIE Symposium. SPIE, vol. 2419, pp. 14–25 (1995)
Nagasaka, A., Tanaka, Y.: Automatic video indexing and full-video search for object appearances. In: Proc. of IFIP TC2/WG2.6, pp. 113–127 (1991)
Pass, G., Zabih, R., Miller, J.: Comparing images using colour coherence vectors. In: Proc. Of the Fourth ACM Multimedia Conference, pp. 65–73 (1996)
Puzicha, J., Rubner, Y., Tomasi, C., Buhmann, J.M.: Empirical Evaluation of Dissimilarity Measures for Color and Texture. In: IEEE ICCV, Greece, pp. 1165–1172 (1999)
Ren, W., Singh, M., Singh, S.: Automated video segmentation. In: Proc. 3rd International Conference on Information, Communications & Signal Processing (2001)
Rubner, Y., Tomasi, C., Guibas, L.J.: The Earth Mover’s Distance as a metric for image retrieval. IJCV Journal, 99–121 (2000)
Sethi, I.K., Patel, N.: A statistical approach to scene change detection. SPIE 2420, 329–339 (1995)
Song, H.S., Kim, I.K., Cho, N.I.: Scene change detection by feature extraction from strong edge blocks. Proc. of SPIE 4671, 484–492 (2002)
Truong, B.T., Dorai, C., Venkatesh, S.: New enhancements to cut, fade, and dissolve detection in video segmentation. In: ACM Multimedia 2000, pp. 219–227 (2000)
Yeo, B.L., Liu, B.: Rapid scene analysis on compressed video. IEEE Transactions on Circuits and Systems for Video Technology 5, 533–544 (1995)
Yeo, B.L., Liu, B.: A unified approach to temporal segmentation of motion JPEG and MPEG compressed video. In: Proc. IEEE ICMCS, pp. 81–88 (1999b)
Webb, A.: Statistical Pattern Recognition. Arnold, London (1999)
Yusoff, Y., Christmas, W., Kittler, J.: Video shot cut detection using adaptive thresholding. In: Proc. British Machine Vision Conference (2000)
Yusoff, Y., Christmas, W., Kittler, J.: A study on automatic shot change detection. In: ECMAST 1998. LNCS, vol. 1425, pp. 177–189. Springer, Heidelberg (1998)
Yu, J., Srinath, M.D.: An efficient method for scene cut detection. Pattern Recognition Letters 22, 1379–1391 (2001)
Zabih, R., Miller, J., Mai, K.: A feature-based algorithm for detecting and classifying scene breaks. In: Proc. ACM Multimedia, pp. 189–200 (1995)
Zabih, R., Miller, J., Mai, K.: A feature-based algorithm for detecting and classification production effects. Multimedia Systems 7, 119–128 (1999)
Zhang, J., Kankanhalli, A., Smoliar, S.W.: Automatic partitioning of full-motion video. Multimedia Systems 1, 10–28 (1993)
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Ren, W., Singh, S. (2004). Automatic Video Shot Boundary Detection Using Machine Learning. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_42
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DOI: https://doi.org/10.1007/978-3-540-28651-6_42
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