TY - GEN
T1 - Graph-FCN for Image Semantic Segmentation
AU - Lu, Yi
AU - Chen, Yaran
AU - Zhao, Dongbin
AU - Chen, Jianxin
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Graph convolutional network
KW - Graph neural network
KW - Semantic segmentation
UR - http://www.scopus.com/inward/record.url?scp=85068614822&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-22796-8_11
DO - 10.1007/978-3-030-22796-8_11
M3 - Conference Proceeding
AN - SCOPUS:85068614822
SN - 9783030227951
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 97
EP - 105
BT - Advances in Neural Networks – ISNN 2019 - 16th International Symposium on Neural Networks, ISNN 2019, Proceedings
A2 - Lu, Huchuan
A2 - Tang, Huajin
A2 - Wang, Zhanshan
PB - Springer Verlag
T2 - 16th International Symposium on Neural Networks, ISNN 2019
Y2 - 10 July 2019 through 12 July 2019
ER -