Image segmentation method based on organizational evolutionary cluster algorithm

Abstract

The invention discloses an image segmentation method based on an organizational evolutionary cluster algorithm, for mainly solving the problems of sensitivity of an initial cluster center, slow convergence speed and proneness to falling into a local extreme value in the prior art. The method transforms image cluster segmentation into a global optimization issue. The realization steps comprises: first of all, combining an organizational evolutionary heredity algorithm (OEA) with a fuzzy C-means (FCM) cluster algorithm, at the same time, by use of pixel point space information, obtaining an optimal cluster center and a membership grade matrix through the global optimization capability of the OEA; and outputting a luster label according to a maximum membership grade principle so as to realize image segmentation. The advantages are as follows: the noise-immune capability is high, the convergence speed is high, the image segmentation quality and the stability of segmentation effects can be improved, and the method can be applied to the extraction and identification of an image target.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to an image segmentation method, in particular to an image segmentation method based on a tissue evolution clustering algorithm, which 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 en...

AI Technical Summary

Problems solved by technology

However, the disadvantage of this FCM algorithm is that it is sensitive to the initial value 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 satisfactory results. Published in 2004) has done a lot of research on this, but due to the limitation 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

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