Video Image Segmentation and Understanding

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Abstract:

Video object extraction is a critical task in multimedia analysis and editing. Normally, the user provides some hints of foreground and background, and then the target object is extracted from the video sequence. In this paper, we propose a object segmentation system that integrates a clustering model with Markov random field-based contour tracking and graph-cut image segmentation. The contour tracking propagates the shape of the target object, whereas the graph-cut refines the shape and improves the accuracy of video segmentation. Experimental results show that our segmentation system is efficient

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Periodical:

Advanced Materials Research (Volumes 424-425)

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151-154

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Online since:

January 2012

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] C. G. M. Snoek and M. Worring: Concept-based video retrieval, Trends Inf. Retriev., vol. 4, no.

Google Scholar

[2] R. Cucchiara, C. Grana, M. Piccardi, and A. Prati, Detecting moving objects, ghosts, and shadows in video streams, IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 10, p.1337–1342, Oct. (2003).

DOI: 10.1109/tpami.2003.1233909

Google Scholar

[3] Z. Zivkovic and F. van der Heijden, Efficient adaptive density estima-tion per image pixel for the task of background subtraction, Pattern Recognit. Lett., vol. 27, no. 7, p.773–780, (2006).

DOI: 10.1016/j.patrec.2005.11.005

Google Scholar

[4] A. Elgammal, D. Harwood, and L. Davis, Non-parametric model for background subtraction, in Proc. 6th Eur. Conf. Comput. Vision, Jun. –Jul. 2000, p.751–767.

DOI: 10.1007/3-540-45053-x_48

Google Scholar

[5] O. Javed, K. Shafique, and M. Shah, A hierarchical approach to robust background subtraction using color and gradient information, in Proc. MOTION, 2002, p.22–27.

DOI: 10.1109/motion.2002.1182209

Google Scholar

[6] K. Toyama, J. Krumm, B. Brumitt, and B. Meyers, Wallflower: Princi-ples and practice of background maintenance, in Proc. Int. Conf. Comp. Vision, 1999, p.255–261.

Google Scholar

[7] B. Stenger, V. Ramesh, N. Paragios, F. Coetzec, and J. M. Buh-mann, Topology free hidden Markov models: Application to back-ground modeling, in Proc. Int. Conf. Comput. Vision, 2001, p.294–301.

DOI: 10.1109/iccv.2001.937532

Google Scholar

[8] N. Paragios and V. Ramesh, A MRF-based real-time approach forsubway monitoring, in Proc. CVPR, 2001, p.1034–1040.

Google Scholar

[9] C. -H. Teh and R. -T. Chin, On the detection of dominant points on digital curves, IEEE Trans. PAMI, vol. 11, no. 8, p.859–872, Aug. (1989).

DOI: 10.1109/34.31447

Google Scholar