Fuzzy C kernel mean clustering segmentation method based on improved whale algorithm optimization

A mean clustering and whale technology, which is applied in the field of fuzzy C kernel mean clustering and segmentation, can solve problems such as the inability to effectively suppress speckle noise information, the time-consuming solution of cluster centers, and being easily affected by the initial cluster center.

Pending Publication Date: 2020-12-25
CHANGSHA SOCIAL WORK COLLEGE +1
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Problems solved by technology

However, the fuzzy C-kernel-means clustering algorithm is easily affected by the initial cluster center during the solution process, thus falling into local optimum
[0003] The existing fuzzy C-means...

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  • Fuzzy C kernel mean clustering segmentation method based on improved whale algorithm optimization
  • Fuzzy C kernel mean clustering segmentation method based on improved whale algorithm optimization
  • Fuzzy C kernel mean clustering segmentation method based on improved whale algorithm optimization

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[0019] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0020] like figure 1 Shown: A fuzzy C-kernel-means clustering and segmentation method optimized based on the improved Whale algorithm, including the following steps;

[0021] (1) input image X={x 1 , x 2 ,...,x n};

[0022] (2) Set the relevant parameters of the KFCM algorithm and the whale optimization algorithm, including the maximum number of iterations, the number of clusters, the number of populations N, the logarithmic spiral shape constant b, the random number l and the algorithm termination condition;

[0023] (3) Initialize the whale position, and use the cluster center as the initial position of the whale in the whale optimization algorithm;

[0024] (4) According to the formula

[0025] Separately calculate the partition membership matrix and cluster centers

[0026] (5) According to the formula Calculate the fitness value of each wh...

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Abstract

The invention discloses a fuzzy C-kernel mean clustering segmentation method based on improved whale algorithm optimization, and the method comprises the steps: inputting an image, setting parameters,initializing whale positions, calculating the adaptive value of each whale, determining an asynchronous communication mechanism, and finally outputting the optimal whale position. According to the method, an asynchronous communication strategy and a selection mechanism are introduced to improve the whale algorithm, so that the convergence speed and precision of the algorithm are further improved;and segmenting the synthetic aperture radar image. A test result shows that the algorithm has good segmentation quality and can realize rapid segmentation of the SAR image at the same time.

Description

technical field [0001] The invention relates to the field of algorithm optimization technology and image processing, in particular to a fuzzy C-kernel mean value clustering and segmentation method based on an improved whale algorithm optimization. Background technique [0002] Fuzzy C-kernel-means clustering algorithm is an important clustering algorithm based on objective function optimization. The design of the algorithm model is intuitively corresponding to the actual problem, easy to be implemented by computer, and the calculation is simple, and it has good clustering performance. However, the fuzzy C-kernel-means clustering algorithm is easily affected by the initial cluster center during the solution process, and thus falls into a local optimum. [0003] The existing fuzzy C-means clustering algorithm cannot effectively suppress the problem of a large number of speckle noise information in synthetic aperture radar images, and the calculation of the cluster center is ti...

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Application Information

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IPC IPC(8): G06K9/62G06N3/00G06K9/34
CPCG06N3/006G06V10/267G06F18/23
Inventor 雷翔霄唐春霞陈朝廷李玲王艳玲谢甘霖胡国强
Owner CHANGSHA SOCIAL WORK COLLEGE
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