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.
Highlights
In recent years, deep learning has achieved revolutionary changes and success in the field of machine learning and data mining, especially for the processing of unstructured data, such as image recognition, natural language processing, and machine translation, which has made full progress and development, making artificial intelligence technology to a new level
Speech, and other unstructured data, the processing method of the deep learning algorithm is to simplify it into grid data forms, such as multidimensional array or multidimensional vector, which is used for data modeling, recognition, and processing
In reality, there are many data that do not conform to the Euclidean domain, such as e-commerce data, social network data, and protein molecular spatial structure. ese data have complex spatial topological relationships, which cannot be treated as tensor data such as multidimensional array or multidimensional vector. erefore, similar data with spatial structure and spatial connection is called “nonEuclidean” data
Summary
Image semantic data have multilevel feature information. 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. E graph convolution network is used to construct the image search process. 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. E results show that the segmentation algorithm designed in this paper has the highest accuracy
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