Image division method based on biogeography optimization

An image segmentation and geographic technology, applied in the field of image processing, can solve problems such as no image segmentation algorithm, achieve the effect of overcoming initialization sensitivity, improving stability and clustering performance, and reducing the amount of calculation

Inactive Publication Date: 2013-09-04
HARBIN ENG UNIV
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  • Image division method based on biogeography optimization
  • Image division method based on biogeography optimization
  • Image division method based on biogeography optimization

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[0036] The technical solution of the present invention is to introduce the biogeographical migration strategy into the clustering algorithm to achieve better clustering performance, and proposes a cluster center optimization method based on the biogeographical migration strategy to solve the problem caused by the uncertainty of the fuzzy clustering center Computational complexity problem, get a new image segmentation method.

[0037] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0038] Step 1: Initialize multiple parameters.

[0039] The number of categories C is artificially determined, and the number of categories to be segmented is also different depending on the picture.

[0040] The fuzzy index m is based on the most frequently used and recognized optimal value at the current stage: 2.

[0041] The group size n is artificially determined, and it is set as 30 in this paper; the number of iterations N is 500; the elite r...

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Abstract

The invention belongs to the technical field of image processing, in particular to an image division method based on biogeography optimization capable of being used for image enhancement, mode identification, target tracking and the like. The method comprises the following steps that parameters are initialized; pictures to be divided are input, and clustering centers are initialized; a fuzzy matrix is calculated; the clustering centers are calculated again; the fitness value of each clustering center is extracted; the immigration rate and the emigration rate of each emigration center are extracted; the emigration centers are updated according to mutation operators; and division results are output. Because the biogeography migration strategy is adopted for optimizing the clustering centers, the calculation quantity is reduced, the selected clustering centers after the optimization have the overall situation characteristics, the problem of initialization sensitivity of the traditional clustering algorithm is solved, and the stability and the clustering performance of the clustering algorithm are improved. Because the immigration and emigration strategies are introduced for optimizing the clustering centers, the data center distribution characteristics can be more accurately reflected, in addition, the corresponding clustering center updating rules are designed, and the calculation quantity is reduced.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image segmentation method that can be used in technical fields such as image enhancement, pattern recognition, and target tracking. Background technique [0002] Image segmentation technology has always been one of the most difficult and most difficult problems in machine vision and multimedia application technology. This is because, so far, researchers have not developed a general, reliable automatic segmentation algorithm. The so-called image segmentation technology, the existing analysis methods are only for images in a certain field or a certain type of images. Therefore, in the next few years, the problem that needs to be solved urgently in the field of image technology is still the technical breakthrough of image segmentation. [0003] The application of fuzzy clustering algorithm in the field of image segmentation has become one of the most extens...

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Application Information

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IPC IPC(8): G06T7/00G06K9/62G06N3/00
Inventor 徐立芳莫宏伟李真真雍升胡嘉祺孟龙龙孙泽波
Owner HARBIN ENG UNIV
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