论文标题
图像语义分割的图形-FCN
Graph-FCN for image semantic segmentation
论文作者
论文摘要
深度学习的语义分割在对图像中的像素分类中取得了巨大进步。但是，在深度学习中，通常在高级特征提取中忽略了本地位置信息，这对于图像语义分割很重要。为了避免此问题，我们提出了一个由名为图像语义分割的Graph-FCN初始初始卷积网络（FCN）初始初始化的图形模型。首先，图像网格数据通过卷积网络扩展到图形结构数据，该网络将语义分割问题转换为图节点分类问题。然后，我们应用图形卷积网络来解决此图节点分类问题。据我们所知，这是我们第一次将图形卷积网络应用于图像语义分割。与原始FCN模型相比，我们的方法在VOC数据集的平均交叉点（MIOU）（MIOU）（MIOU）（MIOU提高约1.34％）中实现了竞争性能。
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.
