NDLI: Human object segmentation using Gaussian mixture model and graph cuts

Content Provider	IEEE Xplore Digital Library
Author	Baoyan Ding Ran Shi Zhi Liu Zhaoyang Zhang
Copyright Year	2010
Description	Author affiliation: School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China (Baoyan Ding; Ran Shi; Zhi Liu; Zhaoyang Zhang)
Abstract	In this paper, we propose an efficient approach to automatic human object segmentation. First, foreground (human object) model and background model are built based on the face detection result, and are used to obtain the seed pixels for foreground and background, respectively. Then seed pixels are clustered using K-means algorithm, and Gaussian mixture models are exploited to generate the foreground/background probability map. Finally, pixels are efficiently classified into foreground and background under the framework of graph cuts. Experimental results on a variety of video sequences demonstrate the better segmentation performance of the proposed approach.
Starting Page	787
Ending Page	790
File Size	192531
Page Count	4
File Format	PDF
ISBN	9781424458561
e-ISBN	9781424458585
DOI	10.1109/ICALIP.2010.5685092
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-11-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Video sequences Humans Object segmentation Face detection Face Pixel
Content Type	Text
Resource Type	Article

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Human object segmentation using Gaussian mixture model and graph cuts