DocumentCode :
3637837
Title :
Motion segmentation in compressed video using Markov Random Fields
Author :
Yue-Meng Chen;Ivan V. Bajić;Parvaneh Saeedi
Author_Institution :
School of Engineering Science, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada
fYear :
2010
Firstpage :
760
Lastpage :
765
Abstract :
In this paper, we propose an unsupervised segmentation algorithm for extracting moving objects/regions from compressed video using Markov Random Field (MRF) classification. First, motion vectors (MVs) are quantized into several representative classes, from which MRF priors are estimated. Then, a coarse segmentation map of the MV field is obtained using a maximum a posteriori estimate of the MRF label process. Finally, the boundaries of segmented moving regions are refined using color and edge information. The algorithm has been validated on a number of test sequences, and experimental results are provided to demonstrate its superiority over state-of-the-art methods.
Keywords :
"Motion segmentation","Pixel","Quantization","Markov random fields","Computer vision","Image edge detection","Color"
Publisher :
ieee
Conference_Titel :
Multimedia and Expo (ICME), 2010 IEEE International Conference on
ISSN :
1945-7871
Print_ISBN :
978-1-4244-7491-2
Electronic_ISBN :
1945-788X
Type :
conf
DOI :
10.1109/ICME.2010.5583034
Filename :
5583034
Link To Document :
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