Online-GCN: an online interactive segmentation method based on graph convolutional network

Interactive image segmentation can improve segmentation performance using manual intervention. Traditional interactive segmentation methods have unsatisfactory segmentation accuracy for images with complex background. Deep learning-based methods depend on large and accurate annotated datasets. In this paper, we propose an online interactive segmentation method based on graph convolutional network (GCN), which includes the superiorities of these two types of methods. We present a pre-segmentation stage to get an initial segmentation of the image, then propose an interactive GCN (iGCN) module to further improve the accuracy of the initial segmentation. Moreover, iGCN module is trained online without any pre-training burden. Experimental results show that our method outperforms several state-of-the-art methods on GrabCut and Berkeley datasets.