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Agent-driven multi-objective evolutionary fuzzy clustering method based on preference information

A fuzzy clustering method and multi-objective evolution technology, applied in the field of image processing, can solve the problems of low calculation efficiency and long time consumption of the algorithm, and achieve better segmentation effect, reduce time cost, and improve evolution efficiency

Pending Publication Date: 2021-11-30
XIAN UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0004] The purpose of the present invention is to address the above-mentioned deficiencies in the prior art, and propose an agent-driven multi-objective evolutionary fuzzy clustering method based on preference information, which is used to solve the low computational efficiency of the algorithm when the multi-objective evolutionary fuzzy clustering algorithm is applied to image segmentation , time-consuming technical issues

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  • Agent-driven multi-objective evolutionary fuzzy clustering method based on preference information
  • Agent-driven multi-objective evolutionary fuzzy clustering method based on preference information
  • Agent-driven multi-objective evolutionary fuzzy clustering method based on preference information

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Embodiment 1

[0058] Refer to attached figure 1 , an agent-driven multi-objective evolutionary fuzzy clustering method based on preference information proposed by the present invention, which generally includes the following steps:

[0059] Step A. Input the image to be segmented and set the initial parameter values.

[0060] Input the image I to be divided;

[0061] Set initial parameter values: population size E=50, fuzzy index m=2, maximum iteration number of fine search TF max =50, the threshold σ=0.3, the reference point is (0.1,0.9), the maximum number of iterations of rough search TC max =20, the crossover probability is 0.9, the mutation probability is 0.1, the coefficient β=0.3 to control the dimension of the coarse search space, the weight factor χ=0.5 to control the image region information, and the number of reference vectors v=40;

[0062] Step B: Apply the multi-objective evolutionary fuzzy clustering method based on reference points and preference angles guided by coarse a...

Embodiment 2

[0080] Refer to attached figure 1 , the method proposed by the present invention specifically includes the following steps:

[0081] Step 1: Input the image I to be segmented, and set the initial parameter values: population size E=50, fuzzy index m=2, maximum iteration number TF of fine search max =50, the threshold σ=0.3, the reference point is (0.1,0.9), the maximum number of iterations of rough search TC max =20, the crossover probability is 0.9, the mutation probability is 0.1, the coefficient β=0.3 to control the dimension of the coarse search space, the weight factor χ=0.5 to control the image region information, and the number of reference vectors v=40;

[0082] Step 2: Use Latin hypercube sampling to obtain the initial population X, and encode the chromosomes in the population;

[0083] Step 3: Decode each encoded chromosome to obtain a column vector, calculate the fitness function value Y corresponding to each chromosome, specifically calculate the global fuzzy com...

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Abstract

The invention discloses an agent-driven multi-objective evolutionary fuzzy clustering method based on preference information, and mainly solves the problems that the existing color image segmentation performance is not ideal and the calculation cost is high. The scheme comprises the steps of inputting a to-be-segmented image and setting an initial parameter value; constructing a fitness function fusing image region information, introducing a thickness agent model, and designing a multi-objective evolution clustering framework based on a dominating relation of a reference point and a preference angle to obtain a non-dominating solution set; constructing a clustering effectiveness index by using information entropy of a fuzzy membership function, and selecting an optimal chromosome from the non-dominated solution set by using the index to decode the optimal chromosome so as to obtain an optimal clustering center; updating the global membership matrix by using the optimal clustering center, and obtaining a classification result of the pixel points according to a maximum membership principle. For an image with a complex background and a low contrast ratio, the image segmentation effect can be effectively improved, and the consumed time is short; the method can be used for natural image recognition.

Description

technical field [0001] The invention belongs to the field of image processing, and further relates to a fuzzy clustering image segmentation method, specifically an agent-driven multi-objective evolutionary fuzzy clustering method based on preference information, which can be used for natural image recognition. Background technique [0002] Image segmentation is to divide the image into several disjoint regions according to features such as grayscale, color, texture and geometric shape, so that the features show similarity in the same region and obvious differences between different regions. Image segmentation algorithms mainly include threshold-based segmentation algorithms, cluster-based segmentation algorithms, edge detection-based segmentation algorithms, and convolutional neural network-based weakly supervised learning segmentation algorithms. Common clustering methods can be mainly divided into K-means clustering algorithm, fuzzy clustering algorithm, density-based clus...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06K9/34G06N3/00
CPCG06N3/006G06F18/23Y02T10/40
Inventor 赵凤刘非凡刘汉强
Owner XIAN UNIV OF POSTS & TELECOMM
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