Image segmentation method based on strong and weak joint semi-supervised intuitionistic fuzzy clustering

Abstract

The invention discloses an image segmentation method based on strong and weak joint semi-supervised intuitionistic fuzzy clustering, and mainly solves the problems that the existing image segmentation is sensitive to an initial value, is easy to fall into local optimum, and is linearly inseparable to low-dimensional data. According to the scheme, a to-be-segmented image is input, initial parameters are set, and manual lineation is carried out; carrying out intuitive fuzzy processing on the image; designing a strong and weak combined semi-supervised strategy to obtain a strong supervised membership degree, a weak supervised membership degree and an initial clustering center; introducing the kernel function, the strong supervision membership degree and the weak supervision membership degree into an intuitionistic fuzzy clustering objective function to obtain a strong and weak combined semi-supervised kernel intuitionistic fuzzy clustering objective function; minimizing the objective function by adopting a Lagrange multiplier method to calculate a clustering optimal solution; and obtaining a classification result of the image pixel points according to a maximum membership degree principle. According to the method, the sensitivity to an initial value is improved, local optimum is prevented, the segmentation accuracy of linear inseparable data is improved, and the method can be used for natural image recognition.

Description

technical field [0001] The invention belongs to the field of digital image processing, in particular to an image segmentation method, which can be used for natural image recognition and computer vision preprocessing. Background technique [0002] As a pivotal link between image processing and subsequent image understanding, image segmentation has always been a hot research topic by scholars, and it occupies an increasingly important position. The purpose of image segmentation is to divide the image into several sub-regions with different attributes and no intersection according to its own characteristics. Each pixel in each sub-region has different degrees of similar characteristics. There are also significant differences. In recent years, image segmentation technology has provided reliable and effective help in the fields of satellite remote sensing, intelligent security, unmanned driving, medical image processing and biometric identification. In the process of practical ...

AI Technical Summary

Problems solved by technology

There are two problems in this method when implementing image segmentation: one is that it does not use a small amount of prior information that can be obtained manually, which leads to its blind search for the optimal solution, and it is easy to fall into the local optimum, resulting in the background distribution. The uneven image segmentation performance is not ideal; the second is that more blur and uncertainty in the image are not considered, making the segmentation of some blur pixels inaccurate

However, because this method still does not consider more ambiguity of the data and does not make full use of the artificial prior information, it is sensitive to the initial value, easy to fall into the local optimal solution, and has poor performance for image segmentation with uneven background distribution. ideal question

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