Abstract
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.
Highlights
Image segmentation is a fundamental problem for many computer vision tasks, such as object recognition [1,2], image understanding [3], and retrieval [4]
We propose a new Markov random field (MRF)-based cosegmentation method namely mutual GrabCut (MGrabCut) for common object segmentation, which extends GrabCut [21] to solve co-segmentation
The experimental results demonstrate the effectiveness of the proposed method
Summary
Image segmentation is a fundamental problem for many computer vision tasks, such as object recognition [1,2], image understanding [3], and retrieval [4]. The existing co-segmentation models address cosegmentation as an optimization problem, which achieves common objects by adding foreground similarity into segmentation models. We propose a new MRF-based cosegmentation method namely mutual GrabCut (MGrabCut) for common object segmentation, which extends GrabCut [21] to solve co-segmentation. The proposed method is robust to initial curve setting because the common objects can be more accurately located by the constraint of foreground similarity. The foreground similarity constraint is added into unary term rather than global term, which results in the efficient minimization by graph cut algorithm. In the existing co-segmentation methods, cosegmentation is commonly modeled as an optimization problem, which introduces foreground similarity to fit common object segmentation. The co-segmentation problem was formulated as the shortest path problem and was solved by dynamic programming method
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