Abstract
As a means of regulating people’s code of conduct, law has a close relationship with text, and text data has been growing exponentially. Managing and classifying huge text data have become a huge challenge. The PDES image segmentation algorithm is an effective natural language processing method for text classification management. Based on the study of image segmentation algorithm and legal case text classification theory, an image segmentation model based on partial differential equation is proposed, in which diffusion indirectly acts on level set function through auxiliary function. The software architecture of image segmentation algorithm text classification system is proposed by using computer technology and three-layer architecture model, which can improve the classification ability of text classification algorithm. The validity of pDE image segmentation model is verified by experiments. The experimental results show that the model completes the legal case text classification, the performance of each functional module of the legal case text classification system is good, and the efficiency and quality of the legal case text classification are improved.
Highlights
Image segmentation is the basis of computer vision and other high-level image processing and is the key step of image recognition and registration
The image segmentation method based on partial differential equation has been welcomed by scholars at home and abroad, and the application of image segmentation technology to text classification analysis has a broader prospect
This paper introduces the significance of image segmentation technology and extends the research of image segmentation method based on partial differential equation
Summary
Image segmentation is the basis of computer vision and other high-level image processing and is the key step of image recognition and registration. In order to minimize the energy functional, the numerical solution of the equation is used to solve the equation, and the numerical solution of the equation is the desired segmentation curve [2] This kind of method can deal with the change of topology structure of evolution curve effectively and can deal with the given image directly and does not need a lot of training data, repeated adjustment, and network learning. According to the derivation of partial differential equation, active contour can be divided into two types: the partial differential equation model directly designed based on evolution theory and the partial differential equation model derived based on energy functional problem The former introduces the design equation of time variable by analyzing the change of image in the process of segmentation, while the latter obtains the energy functional of the objective function by analyzing the properties of the image to be segmented and transforms the image segmentation problem into an energy functional minimum problem under certain constraints. The difference function obtained has high accuracy and smoothness and overcomes the shortcomings of traditional algorithms such as repeated reinitialization and sensitivity to initial contour position
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