Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-22796-8_11 | ADVANCES IN NEURAL NETWORKS - ISNN 2019, PT I |
Keywords | Field | DocType |
Graph neural network, Graph convolutional network, Semantic segmentation | Graph,Pattern recognition,Computer science,Segmentation,Feature extraction,Artificial intelligence,Pixel,Deep learning,Grid,Graph model,Graph Node | Conference |
Volume | ISSN | Citations |
11554 | 0302-9743 | 2 |
PageRank | References | Authors |
0.36 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Lu | 1 | 8 | 12.85 |
| Yaran Chen | 2 | 52 | 6.66 |
| Dongbin Zhao | 3 | 57 | 5.42 |
| Jianxin Chen | 4 | 33 | 6.69 |