Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/79570
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Type: Journal article
Title: A mutual GrabCut method to solve co-segmentation
Author: Gao, Z.
Shi, P.
Karimi, H.
Pei, Z.
Citation: Eurasip Journal on Image and Video Processing, 2013; 2013(1):20-1-20-11
Publisher: SpringerOpen
Issue Date: 2013
ISSN: 1687-5281
1687-5281
Statement of
Responsibility: 
Zhisheng Gao, Peng Shi, Hamid Reza Karimi and Zheng Pei
Abstract: Co-segmentation aims at segmenting common objects from a group of images. Markov random field (MRF) has been widely used to solve co-segmentation, which introduces a global constraint to make the foreground similar to each other. However, it is difficult to minimize the new model. In this paper, we propose a new Markov random field-based co-segmentation model to solve co-segmentation problem without minimization problem. In our model, foreground similarity constraint is added into the unary term of MRF model rather than the global term, which can be minimized by graph cut method. In the model, a new energy function is designed by considering both the foreground similarity and the background consistency. Then, a mutual optimization approach is used to minimize the energy function. We test the proposed method on many pairs of images. The experimental results demonstrate the effectiveness of the proposed method.
Keywords: Co-segmentation; GrabCut; graph cut algorithm, Markov fandom field
Description: Extent: 11 p.
Rights: © 2013 Gao et al.; licensee Springer. This is an Open Access article distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproductionin any medium, provided the original work is properly cited.
RMID: 0020130293
DOI: 10.1186/1687-5281-2013-20
Appears in Collections:Electrical and Electronic Engineering publications

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