Image cutting method based on multi-target intelligent body evolution clustering algorithm

An image segmentation and clustering algorithm technology, which is applied in image analysis, image enhancement, image data processing, etc., can solve the problem of low algorithm robustness, achieve accuracy, improve stability, and overcome unstable segmentation results Effect

Active Publication Date: 2015-04-22
XIDIAN UNIV
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to overcome the above-mentioned deficiencies that are easy to fall into local optimum and algorithm robustness is not high, propose a kind of image segmentation meth

Method used

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  • Image cutting method based on multi-target intelligent body evolution clustering algorithm
  • Image cutting method based on multi-target intelligent body evolution clustering algorithm
  • Image cutting method based on multi-target intelligent body evolution clustering algorithm

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Experimental program
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Embodiment 1

[0033] refer to figure 1 , the present invention is an image segmentation method based on a multi-objective agent evolutionary clustering algorithm, and the specific implementation steps are as follows:

[0034] Step 1. Input the image to be segmented and extract the grayscale information of the image to be segmented. If the input image is not a grayscale image, it needs to be preprocessed to convert it into a grayscale image. The grayscale information is marked as data.

[0035]Step 2. Set the initial cluster number c and the upper limit of iterations T, and perform random initialization of the clustering prototype for the image grayscale information. The clustering prototype is the initial clustering result of the image grayscale information, and the random initialization of the clustering prototype means to randomly specify c pixel values ​​as clustering centers, save them in the image agent, and classify the pixels of the image to the clustering center with the smallest pi...

Embodiment 2

[0066] The image segmentation method based on the multi-objective agent evolutionary clustering algorithm is the same as in embodiment 1, wherein a new image agent grid L is formed in step 5 t+1 / 2 The process is to apply the neighborhood competition operator to the image agents in the set level2 in turn, and merge the obtained new image agents with the image agents in the set level1, and then form a new image agent network together after the merger Grid L t+1 / 2 . Neighborhood competition process is based on neighborhood competition operators to generate new image agents with two strategies, the overall process includes:

[0067] Neighborhood competition operator generates a new image agent according to one of the following two strategies

[0068] Strategy 1, generate image agents according to

[0069]

[0070]where p=1,2,...,c,e p for elements in the is the lower bound of all image agent element values, is the upper bound of all image agent element values, m ...

Embodiment 3

[0083] The image segmentation method based on multi-objective agent evolutionary clustering algorithm is the same as embodiment 1-2, wherein the second generation non-dominated image agent L is obtained in step 8 b , b is the number of the second-generation non-dominated image agent, b=1,2,...,A, 1≤A≤L size × L size The process is through the non-dominated image agent L a , a=1,2,...,A, 1≤A≤L size × L size It is obtained by performing self-learning operator operations in sequence. The self-learning operator operation process is to generate a new image agent through the self-learning operator. The overall process includes:

[0084] The self-learning operator generates an image agent according to the following steps:

[0085] (8.1) Using the image agent grid generation method to generate a self-learning image agent grid sL, its size is sL size ×sL size , sL size is an integer, on which all image agents sL i′,j′ ,i′,j′=1,2,...,sL size Generated according to the followin...

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Abstract

The invention discloses an image cutting method based on a multi-target intelligent body evolution clustering algorithm. The problems that the image cutting technology is prone to local optimum and an algorithm is not high in robustness are mainly solved. The image cutting problem is converted into a global optimization clustering problem. The process includes the steps of extracting gray information of pixel points of an image to be cut, initiating parameters and establishing an image intelligent body network, calculating the energy of an image intelligent body, conducting non-domination sequencing, conducting neighborhood competition operation, conducting Gaussian mutation operation, calculating the energy of the image intelligent body, conducting non-domination sequencing, conducting self-learning operation, selecting the optimal clustering result according to the crowding distance, outputting a clustering label, and achieving image cutting. Multiple targeting is achieved for the image processing process, the convergence effect is good, the robustness of the method is enhanced, the image cutting quality can be improved, the cutting effect stability can be enhanced, and the extraction, recognition and other subsequent processing of the image targets are facilitated.

Description

technical field [0001] The invention belongs to the technical field of image processing, and mainly relates to an image segmentation method, specifically an image segmentation method based on a multi-objective agent evolutionary clustering algorithm, which can be used in the fields of pattern recognition and computer vision. Background technique [0002] Image segmentation is a key technology in image processing. It is widely used in image processing research. For example, target recognition and target measurement are based on image segmentation, and the segmentation results directly affect the follow-up tasks. Therefore, the research on image segmentation is of great significance. Image segmentation is actually a special image processing technology, which is essentially a process of classifying according to the image pixel attributes, namely grayscale, texture, and color. [0003] The clustering-based method is a kind of unsupervised classification, which is widely used in...

Claims

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T7/10G06T2207/20081
Inventor 刘静焦李成王霄刘红英熊涛马晶晶马文萍
Owner XIDIAN UNIV
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