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A Segmentation Algorithm of Image Semantic Sequence Data Based on Graph Convolution Network
Author(s) -
Zheshu Jia,
Deyun Chen
Publication year - 2021
Publication title -
security and communication networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.446
H-Index - 43
eISSN - 1939-0114
pISSN - 1939-0122
DOI - 10.1155/2021/5596820
Subject(s) - computer science , segmentation , segmentation based object categorization , image segmentation , scale space segmentation , artificial intelligence , minimum spanning tree based segmentation , pattern recognition (psychology) , algorithm , graph , convolution (computer science) , computer vision , theoretical computer science , artificial neural network
Image semantic data have multilevel feature information. In the actual segmentation, the existing segmentation algorithms have some limitations, resulting in the fact that the final segmentation accuracy is too small. To solve this problem, a segmentation algorithm of image semantic sequence data based on graph convolution network is constructed. The graph convolution network is used to construct the image search process. The semantic sequence data are extracted. After the qualified data points are accumulated, the gradient amplitude forms complete rotation field and no scatter field in the diffusion process, which enhances the application scope of the algorithm, controls the accuracy of the segmentation algorithm, and completes the construction of the data segmentation algorithm. After the experimental dataset is prepared and the semantic segmentation direction is defined, we compare our method with four methods. The results show that the segmentation algorithm designed in this paper has the highest accuracy.

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