Image segmentation method and system based on effectiveness index of fuzzy clustering guided by three-degree separation

A technology of image segmentation and fuzzy clustering, which is applied in image analysis, image data processing, character and pattern recognition, etc., can solve problems such as unsatisfactory effect and failure to consider data structure, and achieve accurate judgment of clustering number and clustering results accurate effect

Active Publication Date: 2021-11-19
HEFEI UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006]2) Some clustering validity indicators only analyze the degree of membership, such as separation coefficient PC and separation entropy, which were first proposed for fuzzy clustering The PE index only considers the membership degree information,

Method used

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  • Image segmentation method and system based on effectiveness index of fuzzy clustering guided by three-degree separation
  • Image segmentation method and system based on effectiveness index of fuzzy clustering guided by three-degree separation
  • Image segmentation method and system based on effectiveness index of fuzzy clustering guided by three-degree separation

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Embodiment Construction

[0063] In this example, if figure 1 As shown, an image segmentation method for the validity index of fuzzy clustering guided by three-degree separation is carried out according to the following steps:

[0064] Step 1. Use the fuzzy C-means clustering algorithm to set {x 1 ,x 2 ,...,x i ,...,x N} are divided into K classes, and the membership degree matrix U={μ ik | i＝1,2,...,N；k＝1,2,...,K} and cluster center V={v 1 ,v 2 ,...,v k ,...,v K}; where x i Indicates the i-th pixel in the image X, μ ik Represents the i-th pixel x i Belongs to the kth class C k The membership value of , and 0≤μ ik ≤1, v k Represents the cluster center of the kth class; i=1,2,...,N; k=1,2,...,K; N represents the number of pixels in the image X;

[0065] Set the maximum number of iterations as M, the termination condition error of iterations as ε, and initialize K=2;

[0066] Step 2. Use formula (1) to construct the iter-th iteration objective function of the FCM fuzzy algorithm

[0...

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Abstract

The invention discloses an image segmentation method and system based on an effectiveness index of fuzzy clustering guided by three-degree separation. The method comprises the following steps: 1, dividing pixel points in an image by using a fuzzy C-means clustering algorithm; 2, establishing an objective function, and setting a termination condition or reaching the maximum number of iterations; 3, initializing and updating an iteration membership matrix and a clustering center, and judging whether a termination condition is reached or the maximum number of iterations is reached; 4, calculating an inter-class separability value according to a separation relation of three levels, obtaining an intra-class compactness value through a fuzzy weighting distance and a cardinal number of a fuzzy cluster, and obtaining an index value through a ratio of the intra-class compactness value to the intra-class compactness value; 5, comparing the validity indexes of all classes, and selecting the cluster number corresponding to the maximum validity index and the corresponding membership matrix to perform image segmentation. According to the method, the image can be effectively segmented, the pixel points are clustered, an effective clustering result is obtained, and the method is suitable for a complex and overlapped pixel set with noise points.

Description

technical field [0001] The invention belongs to the field of data mining, in particular to an image segmentation method and a system thereof for the effectiveness index of fuzzy clustering guided by three-degree separation. Background technique [0002] The purpose of image segmentation is to extract specific targets from complex images, which is an important basis for image recognition, image understanding and image analysis. With the development of technology, image segmentation based on fuzzy clustering has been widely used in many fields, such as medical image processing, face recognition, traffic road analysis, etc. Therefore, more and more scholars study various indicators about evaluating relevant image segmentation algorithms to judge whether the algorithms are good or bad. Indicators can objectively analyze the practicality of clustering algorithms in certain scenarios. Of course, the results under the measurement of one indicator cannot explain all the problems, ...

Claims

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Application Information

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IPC IPC(8): G06T7/11G06K9/62
CPCG06T7/11G06F18/23213
Inventor 唐益明李冰黄佳佳孙晓李书杰吴文斌陈锐
Owner HEFEI UNIV OF TECH
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