  Semantic segmentation with deep learning has achieved great progress in
classifying the pixels in the image. However, the local location information is
usually ignored in the high-level feature extraction by the deep learning,
which is important for image semantic segmentation. To avoid this problem, we
propose a graph model initialized by a fully convolutional network (FCN) named
Graph-FCN for image semantic segmentation. Firstly, the image grid data is
extended to graph structure data by a convolutional network, which transforms
the semantic segmentation problem into a graph node classification problem.
Then we apply graph convolutional network to solve this graph node
classification problem. As far as we know, it is the first time that we apply
the graph convolutional network in image semantic segmentation. Our method
achieves competitive performance in mean intersection over union (mIOU) on the
VOC dataset(about 1.34% improvement), compared to the original FCN model.
