Gray scale image segmentation method based on sequencing K-mean algorithm

A grayscale image, K-means technology, applied in the field of image processing, can solve the problems of difficult to retain image details, low division efficiency, etc., to achieve the effect of improving accuracy and reducing misclassification rate

Inactive Publication Date: 2012-09-12
陕西国博政通信息科技有限公司
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Problems solved by technology

However, the disadvantage of this method is that when a certain type or several types of pixels in the image are less, it is difficult for this method to retain the image details in the category with more pixels, and the division efficiency is low.

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  • Gray scale image segmentation method based on sequencing K-mean algorithm
  • Gray scale image segmentation method based on sequencing K-mean algorithm
  • Gray scale image segmentation method based on sequencing K-mean algorithm

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[0034] Combine below figure 1 The specific implementation steps of the present invention are further described in detail.

[0035] Step 1, read in a noise-free grayscale image G with a size of 256×256, and randomly specify each cluster center V:

[0036] V=(V 0 , V 1 ,...,V 1 ) among them, V i is the clustering center of the i-th class, i=0,...,n-1, n is the number of clustering categories;

[0037] In the embodiment of the present invention, a noise-free grayscale House image is read in, and the size of the image is 256×256. The images are set to be divided into 4 categories, ie n=4.

[0038] Randomly generate cluster centers V=(V 0 , V 1 , V 2 , V 3 ), the cluster center randomly generated by the present invention is V=(41, 35, 190, 132).

[0039] Step 2, define the gray level histogram HL(l) of the gray level image G:

[0040] HL(l)=n l

[0041] Among them, l is the gray level, l=0,...,255,l i is the total number of pixels of the lth gray level in the graysca...

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Abstract

The invention provides a gray scale image segmentation method based on a sequencing K-mean algorithm according to the defect of difficulty in retaining image details in a category with more pixels in existing K-mean algorithm. The method comprises the following steps: (1) reading in a noise-free gray scale image G and randomly assigning each cluster center; (2) calculating a histogram HL of the read-in gray scale image G; (3) calculating the distances of each gray scale to each cluster center; (4) sequencing the distances of each gray scale to each cluster center; (5) storing the sequenced distances; (6) assigning each gray scale to a cluster center category which is nearest therefrom; (7) updating the cluster centers according to the sequenced distances of each gray scale to each cluster center; and (8) determining whether an iteration stopping condition is achieved according to the updated cluster centers and the non-updated cluster centers, if yes, terminating clustering and outputting the results of clustering to complete image segmentation. The method provided in the invention has the advantages of high precision of image segmentation and capability of being used to extract and obtain detail information of a gray scale image.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to an image segmentation method, in particular to a grayscale image segmentation method, which can be used to extract and obtain detailed information of the grayscale image. Background technique [0002] With the development of computer technology, images are widely used in various industries. Grayscale image segmentation is the basis of obtaining information in the form of images. It is a hot research topic and one of the important contents of image processing technology applications. [0003] Image segmentation is widely used in object recognition, change detection, etc. There are many image segmentation methods, based on gray-scale single-threshold segmentation, gray-scale multi-threshold segmentation, region growth and clustering, etc. [0004] K-means clustering algorithm, referred to as K-means algorithm, was proposed by J.B.MacQueen in 1967. K-means clustering algorithm is a ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
Inventor 尚荣华焦李成白靖靳超吴建设郑喆坤马文萍李阳阳侯彪
Owner 陕西国博政通信息科技有限公司
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