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Image segmenting method based on dynamic multi-population integrated differential evolution algorithm

A differential evolution algorithm and image segmentation technology, applied in image analysis, image data processing, calculation, etc., can solve problems such as easy to fall into local optimum and weak local search ability of population

Active Publication Date: 2018-09-21
HUAQIAO UNIVERSITY +1
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AI Technical Summary

Problems solved by technology

However, for solving the threshold value of high-definition images, the differential evolution algorithm still has some problems, such as weak local search ability of the population and easy to fall into local optimum.

Method used

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  • Image segmenting method based on dynamic multi-population integrated differential evolution algorithm
  • Image segmenting method based on dynamic multi-population integrated differential evolution algorithm
  • Image segmenting method based on dynamic multi-population integrated differential evolution algorithm

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Embodiment Construction

[0053] Such as figure 1 As shown, the present invention is based on the image segmentation method of dynamic multiple clustering into differential evolution algorithm, comprises the following steps:

[0054] Step 1. Set the image segmentation threshold range and the fitness function for evaluating the segmentation effect:

[0055] The image to be segmented is a grayscale image, and the segmentation threshold range is set between 0-255;

[0056] In order to obtain the optimal threshold, the present invention uses the maximum variance between classes as the fitness function for evaluating the segmentation effect. The maximum inter-class variance is an effective evaluation index for image threshold segmentation, which can effectively obtain the optimal threshold f. The calculation formula is as follows:

[0057] f=w 0 ·w 1 ·(u 0 -u 1 ) 2 (1)

[0058] Among them, w 0 is the grayscale probability of the image background, w 1 is the gray level probability of the image tar...

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Abstract

The invention discloses an image segmenting method based on a dynamic multi-population integrated differential evolution algorithm. According to the method, sub-populations are divided and redistributed by adopting an original population in the aspect of population structure planning so as to guarantee the diversity of a population evolution process and avoid a local optimum phenomenon; local search variation is combined with global search variation in the variation strategy so as to achieve a balance between an optimal threshold of population survey and accelerated convergence; and the shortcomings caused by fixed parameters of a standard differential evolution algorithm are effectively overcome through the parabolic dynamic incremental change of a cross probability factor. Compared withother evolutionary algorithms on a benchmark test set, the improved algorithm has the advantage that the optimization and convergence speed can be obviously and significantly improved; and the improved differential evolution algorithm has a significant effect in accuracy and speed when being applied to image segmentation.

Description

technical field [0001] The invention relates to the field of computer simulation and optimization, in particular to an image segmentation method based on a dynamic multiple clustering differential evolution algorithm. Background technique [0002] The threshold method is a simple and effective image segmentation technique, but the threshold method also has obvious disadvantages, that is, for the threshold solution of high-definition images, the amount of calculation increases and the calculation time increases. Differential Evolution (DE) is a heuristic random search algorithm that simulates the evolutionary differences of populations. Storn and Price initially conceived to solve the Chebyshev polynomial problem, and later found that compared with other evolutionary algorithms, the differential evolution algorithm has more outstanding performance in solving complex global optimization problems, and the process is simpler, with fewer controlled parameters and strong adaptabil...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/136G06N3/00
CPCG06N3/006G06T7/136
Inventor 柳培忠范宇凌唐加能骆炎民邓建华杜永兆刘晓芳
Owner HUAQIAO UNIVERSITY
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