Nonsupervision image segmentation process based on clone selection

A technology of clone selection and image segmentation, applied in the field of image processing, can solve problems such as unguaranteed segmentation results, poor reliability, and poor stability

Inactive Publication Date: 2011-04-06
XIDIAN UNIV
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Problems solved by technology

Automatic segmentation does not require human-computer interaction, and the number of segmented regions is automatically determined according to the gray information of the image. The region segmentation based on clustering technology achieves automatic image segmentation to a certain extent, but due to the convergence of the clustering algorithm Especially for images with multiple regions, it is easy to fall into the local optimal solution. This kind of segmentation method cannot guarantee the optimal segmentation results, and the reliability and stability are poor.

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  • Nonsupervision image segmentation process based on clone selection
  • Nonsupervision image segmentation process based on clone selection
  • Nonsupervision image segmentation process based on clone selection

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Embodiment Construction

[0035] The problem of unsupervised image segmentation can be abstracted as the problem of unsupervised clustering of image pixels according to the gray value.

[0036] refer to figure 1 and figure 2 For the specific problem of unsupervised image segmentation, the specific implementation steps of the unsupervised image segmentation method based on clone selection designed by the present invention are as follows:

[0037] Step 1. Antibody group initialization and parameter setting.

[0038] (1a) Read the original image to obtain the following information: grayscale Number of pixels on each gray level: Among them, g i ∈[0, 255] is the gray level contained in the image gray matrix, h i is the gray level g i The number of pixels on, N g is the number of gray levels;

[0039] (1b) Set the antibody group size n and the probability of clonal mutation p m , clone size factor N c , antibody migration probability p j ;Set the iteration condition as follows: the segmentatio...

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Abstract

The invention discloses an unsupervised image segmentation method, which belongs to the technical field of image processing, and aims to reduce the dependence on user apriori knowledge as well as ensure segmentation precision. The method comprises the following steps: (1) initializing an antibody population and setting up parameters; (2) calculating an affinity degree and operating clonal reproduction; (3) carrying out a clonal variation operation on the clonal reproduced antibody population; (4) carrying out a migration operation on the clonal variated antibody population; (5) carrying out an iterative operation on the FCM; (6) calculating the affinity degree of the antibody population and carrying out clonal selection operation; (7) repeating the steps and ending the iterative operationaccording to iterative conditions; and (8) dividing an image according to the antibody optimal and outputting the segmentation result. The method has the advantage that the image can be automaticallysegmented effectively under the circumstance that the amount of the segmentation regions is unspecified.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an unsupervised image segmentation method based on clone selection, which can be applied to automatic segmentation of digital images. Background technique [0002] 80% of the information received by humans comes from visual or image information, including images, graphics, animations, videos, text data, etc. This is the most effective and important way of obtaining and exchanging information. With the popularity of computers, people are increasingly using computers to help humans acquire and process image information. Image processing, image analysis and image understanding, the organic combination of these three levels is called image engineering. [0003] As one of the basic problems of image processing, image segmentation divides the image into a set of non-overlapping regions. These regions are either meaningful to the current task, or help to explain the relations...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/12G06T7/00
Inventor 刘若辰焦李成沈正春王爽公茂果李阳阳马文萍
Owner XIDIAN UNIV
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