Scheduled System Maintenance:
On Monday, April 27th, IEEE Xplore will undergo scheduled maintenance from 1:00 PM - 3:00 PM ET (17:00 - 19:00 UTC). No interruption in service is anticipated.
By Topic

Efficient video object segmentation based on Gaussian mixture model and Markov random field

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Zhi Liu ; Sch. of Commun. & Inf. Eng., Shanghai Univ., Shanghai ; Jiandong Gu, ; Liquan Shen ; Zhaoyang Zhang

This paper proposes an efficient video object segmentation approach based on Gaussian mixture model (GMM) and Markov random field (MRF). The user-interested video objects are interactively extracted in the first frame of the video sequence, and each video object and the remaining background are represented by individual GMM, which is initialized based on the region segmentation result used for interactive object extraction. For each following frame, two GMM classification results are respectively generated based on only color feature, and both color feature and position feature, which is compensated by the estimated average position change to adapt to fast moving regions. Based on the initial pixel classification result generated from the two GMM classification results and the corresponding confidence measures, the pixel classification result is refined to obtain a reliable video object segmentation result under the MRF framework. Experimental results on several MPEG-4 test sequences demonstrate the good segmentation performance of the proposed approach.

Published in:

Signal Processing, 2008. ICSP 2008. 9th International Conference on

Date of Conference:

26-29 Oct. 2008