Method for partitioning genetic fuzzy clustering image

Abstract

The invention discloses a method for partitioning a genetic fuzzy clustering image and provides a method for partitioning a fuzzy clustering image on the basis of a genetic algorithm, which aims to solve the problem that a fuzzy C mean value algorithm is sensitive to noise and is easy to generate an overclosed clustering center due to noise influence. The partitioning method comprises the following steps of: firstly, carrying out noise resistant pretreatment on an original image by a gray level and neighborhood information; then obtaining an initially optimal clustering center by utilizing a genetic fuzzy clustering algorithm; and finally calculating the membership degree of each pixel in an image according to the obtained initially optimal clustering center by a histogram amendment clustering center of the image after noise resistance to obtain a partition result. The method adopts an improved gray level similarity function in the noise resistant pretreatment and ensures the noise resistant effect in noise with larger strength; and a clustering center distance punitive measure is added into the genetic fuzzy clustering algorithm, thereby the image with serious noise interference and a smaller target to be partitioned can be effectively partitioned, and the correct clustering center and an accurate partition result can be obtained.

Description

Technical field [0001] The invention belongs to the field of image processing and application, and specifically relates to an image segmentation method of genetic fuzzy clustering. This method can effectively suppress the noise, and can improve the image segmentation accuracy when the noise interference is serious, and prevent the mis-segmentation caused by the cluster centers being too close. Background technique [0002] Fuzzy C-Means (FCM) is a clustering algorithm with fuzzy decision-making ability. It is very effective for segmentation of fuzzy boundary regions and has been widely used in image processing in recent years. FCM algorithm is easy to converge to local extremum. To solve this problem, Bezdek et al. proposed a method of using genetic algorithm to optimize FCM to ensure the global optimal solution (Bezdek JC, Optimization of fuzzy clustering criteria using genetic algorithms, IEEETransaction on Evolutionary) Computation, 2, 1994:589-594). This method uses genetic...

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Problems solved by technology

[0004] Noise interference will not only cause noise in the segmentation results and affect the accuracy of segmentation, but also interfere with the algorithm's selection of cluster centers

In response to this problem, Krishnapuram et al. (Kirshnapuram R.A., Possibilistic approach to clustering.IEEE Transactions on Fuzzy Systems, 1, 1993:98-100) proposed a Possiblistic Clustering Method (PCM), which removes the single The constraint that the sum of the membership degrees of pixels for each cluster center must be 1 allows some pixels to have a small membership degree relative to each cluster center, so that it does not affect the calculation of the cluster center, but it is difficult to apply PCM alone image segmentation

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