MOTION SEGMENTATION IN COMPRESSED VIDEO USING MARKOV RANDOM FIELDS

被引：6
|
作者：
Chen, Yue-Meng [1 ]
Bajic, Ivan V. [1 ]
Saeedi, Parvaneh [1 ]
机构：
[1] Simon Fraser Univ, Sch Engn Sci, Burnaby, BC V5A 1S6, Canada
来源：
2010 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME 2010) | 2010年
关键词：
Motion segmentation; Markov Random Field; compressed video; OBJECT SEGMENTATION; IMAGES;
D O I：
10.1109/ICME.2010.5583034
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
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.
引用
收藏
页码：760 / 765
页数：6
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