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Color image segmentation method based on local pixel classification

A color image and pixel classification technology, applied in image analysis, image data processing, instruments, etc., can solve the problem of few methods of color image segmentation, and achieve the effect of maintaining connection and correlation and ensuring accuracy.

Inactive Publication Date: 2015-04-08
LIAONING NORMAL UNIVERSITY
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AI Technical Summary

Problems solved by technology

Although people have done a lot of research on image segmentation technology, there are few methods for color image segmentation.

Method used

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  • Color image segmentation method based on local pixel classification
  • Color image segmentation method based on local pixel classification
  • Color image segmentation method based on local pixel classification

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Embodiment Construction

[0033] Such as figure 1 Shown, method of the present invention carries out according to following steps successively:

[0034] A color image segmentation method based on local pixel classification, followed by the following steps:

[0035] Step 1: Select each pixel to construct the color image local window of , use the quaternion PHT moment decomposition to find the moment value of the local window, and construct the characteristics of the pixel;

[0036] Specific steps are as follows:

[0037] Said step 1 is as follows:

[0038] Step 11: For a given original color image m×n, select each pixel A 5×5 local window centered on ;

[0039] Step 12: Calculate the local window The quaternion PHT moment of ;

[0040] Step 121: In order to comprehensively characterize and describe the features of the color image, extend the PHT moment theory of the traditional grayscale image to the quaternion level, and further define the quaternion PHT moment of the color image. suppos...

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Abstract

The invention discloses a color image segmentation method based on local pixel classification. According to the method, firstly, quaternion PHT is used for extracting pixel grade color features; then, ACS-FCM is used for selecting the training samples; finally, a trained TWSVM (twin support vector machine)) model is used for classification, a single super plane is respectively constructed for two kinds of data through using non-parallel planes, the distance of each super plane is possibly close to the current kind of samples and is possibly far away other kinds of samples, a better classification model is obtained, and in addition, the speed is obviously higher than that of a traditional LS-SVM (least squares support vector machine). The method has the advantages that the ACS and the FCM are combined, the overall performance and the robustness of the ACS are utilized for overcoming the defects that the FCM segmentation is not precise enough, and the local optimization can be easily caused. The mutual relations and correlation between image vectors can be well maintained, and in addition, the image pixel features are well described.

Description

technical field [0001] The invention belongs to the technical field of image segmentation for multimedia information processing, in particular to a color image segmentation method based on local pixel classification that can maintain the relationship and correlation between image components and well describe the features of image pixels. Background technique [0002] Image segmentation is to separate the areas with special meaning in the image, which is convenient for target detection, recognition and image retrieval. Although people have done a lot of research on image segmentation technology, there are few methods for color image segmentation. Contents of the invention [0003] The purpose of the present invention is to solve the above technical problems existing in the prior art, and to provide a color image segmentation based on local pixel classification that can maintain the relationship and correlation between image components and well describe the characteristics o...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/10G06T7/40
CPCG06T7/40G06T7/10
Inventor 王向阳孙炜玮牛盼盼
Owner LIAONING NORMAL UNIVERSITY
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