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A mutual GrabCut method to solve co-segmentation

Zhisheng Gao1, Peng Shi23, Hamid Reza Karimi4* and Zheng Pei1

Author Affiliations

1 Center for Radio Administration & Technology Development, Xihua University, Chengdu, Sichuan, 610039, China

2 College of Engineering and Science, Victoria University, Melbourne, Victoria, 8001, Australia

3 School of Electrical and Electronic Engineering, The University of Adelaide

4 Department of Engineering, Faculty of Technology and Science, University of Agder, 4898 Grimstad, Norway

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EURASIP Journal on Image and Video Processing 2013, 2013:20  doi:10.1186/1687-5281-2013-20

Published: 20 April 2013


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.

Co-segmentation; GrabCut; Graph cut algorithm; Markov fandom field