A modified grabcut approach for image segmentation based on local prior distribution
Qiu Guan, Min Hua, Haigen Hu · 2017
GrabCut algorithm is one of most popular approaches of image segmentation. In practice, the segmented image using GrabCut algorithm always keeps too much redundant information of background. Therefore, an improved GrabCut algorithm is proposed in this paper which modifies the energy function of GrabCut algorithm. Firstly, the segmented part of object needs to be chosen as foreground by user and is used to gain local prior distribution. Then, the local prior distribution is used to improve energy function to obtain the primary result of image segmentation rather than the traditional iterative one. Finally, the edge detection of the segmented image is further introduced to remove the redundant region. The results of experiments show that the proposed approach resulted in better performance and segmentation than that of the traditional GrabCut.