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A Segmentation Algorithm of Image Semantic Sequence Data Based on Graph Convolution Network
- Source :
- Security and Communication Networks, Vol 2021 (2021)
- Publication Year :
- 2021
- Publisher :
- Hindawi Limited, 2021.
-
Abstract
- 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.
- Subjects :
- Science (General)
Article Subject
Computer Networks and Communications
Computer science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
02 engineering and technology
Semantic data model
Data segment
Field (computer science)
030218 nuclear medicine & medical imaging
Convolution
Q1-390
03 medical and health sciences
0302 clinical medicine
Data point
Feature (computer vision)
Computer Science::Computer Vision and Pattern Recognition
0202 electrical engineering, electronic engineering, information engineering
T1-995
Graph (abstract data type)
020201 artificial intelligence & image processing
Segmentation
Algorithm
Technology (General)
Information Systems
Subjects
Details
- ISSN :
- 19390122 and 19390114
- Volume :
- 2021
- Database :
- OpenAIRE
- Journal :
- Security and Communication Networks
- Accession number :
- edsair.doi.dedup.....8a11a4b4b66880bce77916ecd77abb10