Multi-agent genetic clustering algorithm-based image segmentation method

Abstract

The invention discloses a multi-agent genetic clustering algorithm-based image segmentation method, which mainly solves the problems that the prior art is sensitive to an initial clustering center, has low convergence rate and is easily trapped in a local extremum. In the method, image clustering segmentation is converted into a global optimization problem. The method comprises the following steps of: firstly, extracting two-dimensional gray scale information of a neighborhood median and a neighborhood mean of pixel points of an image to be segmented to construct a new two-dimensional histogram; secondly, combining a multi-agent genetic algorithm (MAGA) with a fuzzy C-mean (FCM) clustering algorithm and obtaining an optimal clustering center and a membership degree matrix by using the global optimization capability of the MAGA; and finally, outputting clustering tags according to the maximum membership degree principle so as to realize image segmentation. The method has high anti-noise capability and high convergence rate, can improve the image segmentation quality and the stability of a segmentation result and can be used for extracting and identifying image targets.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to an image segmentation method, and can be used in the fields of pattern recognition, computer vision and the like. Background technique [0002] Image segmentation is the basis of subsequent image analysis and image understanding. It has a very wide range of applications in practice. For example, the extraction and measurement of image objects are inseparable from image segmentation. The accuracy of segmentation directly affects the effectiveness of subsequent tasks, so it has very important. [0003] Image segmentation is a special image processing technology, and its essence is a process of classification according to pixel attributes, namely grayscale, texture, and color. [0004] Clustering is a kind of unsupervised classification, which is widely used in fields such as engineering, biology, computer vision and remote sensing. Clustering is to classify a group of data wit...

AI Technical Summary

Problems solved by technology

However, the disadvantage of this FCM algorithm is that it is sensitive to initial values ​​and noise data, and it is easy to fall into local optimum.

In order to solve this kind of problem, many researchers proposed to combine genetic algorithm with FCM, and obtained relatively satisfactory results. Published in 2004) has done a lot of research on this, but due to the limitations of the global evolution mechanism of the traditional genetic algorithm, the method GA-FCM combined with the genetic algorithm and FCM still has slow convergence speed and easy to fall into local extremum And other defects, resulting in the decline of image segmentation quality and the reduction of the stability of the segmentation effect

In addition, the above-mentioned FCM and the method combined with the genetic algorithm and the FCM use the image gray histogram feature when segmenting the image, both of which do not fully consider the spatial information of the image pixels, so the image segmentation quality is easily affected by the image quality. The impact of noise in the middle is not conducive to subsequent image analysis and understanding

Images