Multi-target interval value fuzzy clustering image segmentation method based on double membership driving

Abstract

The invention discloses a multi-target interval value fuzzy clustering image segmentation method based on double membership driving, which mainly solves the problems of being sensitive to noise and easy to fall into local optimum in image segmentation. The scheme includes: inputting an image to be segmented, and setting initial parameter values; constructing an interval value blurred image; constructing a global interval value fuzzy compactness function JLN driven by double membership degrees and an interval value fuzzy separability function SLN driven by double membership degrees, and performing multi-objective evolution on the two objective functions to obtain a non-dominated solution set P; calculating an interval value selective solution index W driven by double membership degrees, and selecting an optimal chromosome from the non-dominated solution set P by using the index to decode the optimal chromosome to obtain an optimal clustering center; and updating the joint membership matrix by using an optimal clustering center, and obtaining a classification result of the pixel points according to a maximum membership principle. According to the method, noise can be effectively inhibited, local optimum is prevented, the segmentation accuracy is improved, and the method can be used for natural image recognition.

Description

technical field [0001] The invention belongs to the field of image processing, and in particular relates to a fuzzy clustering image segmentation method, which can be used for natural image recognition. Background technique [0002] Image segmentation, as the name suggests, is to divide an image into several parts that have different characteristics and are not intersected with each other. It is a key step from image processing to image analysis. In recent years, the research on image segmentation methods has been highly concerned by researchers, including clustering-based methods, threshold-based methods, edge-based methods and region-based methods, among which cluster-based methods are the focus of research. Clustering, as the name implies, is the process of dividing the elements in a collection into multiple classes, in which similar objects are classified into one class, and different objects are classified into different classes. Common clustering methods include K-mea...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to aim at the deficiencies in the prior art, to provide a multi-objective interval-value fuzzy clustering image segmentation method based on dual-membership driving, to reduce the sensitivity to noise, avoid falling into local optimum, and learn from multiple Consider the image segmentation problem and improve the segmentation accuracy

Images