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CNN-G: convolutional neural network combined with graph for image segmentation with theoretical analysis
Lu, Yi1; Chen, Yaran1; Zhao, Dongbin1; Liu, Bao2; Lai, Zhichao2; Chen, Jianxin3
Source PublicationIEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
2021
Volume0Issue:0Pages:0
Abstract

Deep Convolutional Neural Network (CNN), although recognized to be considerably successful in its application to semantic segmentation, are inadequate for extracting the overall structure information, for its representing images with the data in Euclidean space. To improve this inadequacy, a new model in the graph domain that transforms semantic segmentation into graph node classification is proposed for semantic segmentation. In this model, the image is represented by a graph, with its nodes initialized by the feature map obtained by a CNN, and its edges reflecting the relationships of the nodes. The node relationships that are taken into consideration include distance-based ones and semantic ones, respectively calculated with Gauss kernel function and attention mechanism. Graph Neural Network is also introduced in this model for the classification of graph nodes, which can expand the receptive field without the loss of location information and combine the structure with the feature extraction. Most importantly, it is theoretically concluded that the proposed graph model takes the same role as a Laplace regularization term in image segmentation, which has been proven by multiple comparative experiments that show the effectiveness of the model in image semantic segmentation. The learned attention is visualized by the heatmap to validate the structure learning ability of our model.

The results of these experiments show the importance of structure information in image segmentation. Hence an idea of deep learning combined with graph structural information is provided in theory and method.

KeywordGraph neural network, image segmentation, self-attention, structure pattern learning.
DOIDOI:10.1109/TCDS.2020.2998497.
Indexed BySCI
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/40611
Collection复杂系统管理与控制国家重点实验室_深度强化学习
复杂系统管理与控制国家重点实验室
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.Peking Union Medical College Hospital
3.Beijing University of Chinese Medicine
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Lu, Yi,Chen, Yaran,Zhao, Dongbin,et al. CNN-G: convolutional neural network combined with graph for image segmentation with theoretical analysis[J]. IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS,2021,0(0):0.
APA Lu, Yi,Chen, Yaran,Zhao, Dongbin,Liu, Bao,Lai, Zhichao,&Chen, Jianxin.(2021).CNN-G: convolutional neural network combined with graph for image segmentation with theoretical analysis.IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS,0(0),0.
MLA Lu, Yi,et al."CNN-G: convolutional neural network combined with graph for image segmentation with theoretical analysis".IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 0.0(2021):0.
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