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A Complex Network Image Recognition Method Based on Structural Balance Theory

A complex network and image recognition technology, applied in the field of image recognition, can solve problems such as the weakening of the recognition effect, and achieve the effect of improving the operation speed, reducing the number of nodes, and enhancing feature information.

Active Publication Date: 2022-03-11
GUANGDONG UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the above methods are closely related to the position and order of pixels in the image, so the recognition effect will be weakened to varying degrees in the face of image plane rotation invariance, translation invariance, scaling invariance and other characteristics.

Method used

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  • A Complex Network Image Recognition Method Based on Structural Balance Theory
  • A Complex Network Image Recognition Method Based on Structural Balance Theory
  • A Complex Network Image Recognition Method Based on Structural Balance Theory

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Experimental program
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Effect test

Embodiment 1

[0035] A complex network image recognition method based on structural balance theory, specifically including the following steps:

[0036] Step 1: First read the image to get the image matrix , Is a dimension matrix. in represents the number of test images, Indicates the number of pixels in the image, represents the field of real numbers, means transpose;

[0037] Step 2: Construct a complex network model based on the results of step 1;

[0038] Step 3: Calculate complex network parameters based on the complex network model established in step 2: node degree, average degree, maximum degree;

[0039]

[0040]

[0041]

[0042] in is the node degree, is the average degree, is the maximum degree;

[0043] Step 4: In order to simplify the calculation and reduce the storage space, delete all nodes whose degree is greater than the average degree;

[0044] Step 5: Perform numerical normalization on the complex network model obtained in Step 4,

[0045...

Embodiment 2

[0058] Suppose a sample image feature parameter , respectively calculate the Euclidean distance of a certain sample image and the image feature parameter of the test group, and the grouping of the sample image when the Euclidean distance obtains the minimum value is the grouping of the sample image;

[0059] In summary, the complex network image recognition method based on the structural balance theory provided by the present invention can effectively improve the operation speed and the effectiveness of the nodes by establishing a model through the complex network and using the parameters of the complex network to delete similar small nodes in the image. The result of Hadamard product of constructing the structural balance network of the test image and the structural balance matrix is ​​used as the characteristic parameter of image recognition, which makes the parameters of image recognition simple; the useful features in the image are enhanced more effectively by constructing...

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Abstract

The present invention relates to the technical field of image recognition, and in particular to a complex network image recognition method based on the structural balance theory. The complex network image recognition method based on the structural balance theory provided by the present invention establishes a model through a complex network and uses the parameters of the complex network to delete The small similarity nodes in the image can effectively improve the computing speed and the effectiveness of the nodes; by constructing the structural balance network of the test image and the structural balance matrix to do the Hadamard product result as the characteristic parameter of image recognition, the parameters of image recognition become It is simple; by constructing a structural balance matrix, the useful feature information in the image is more effectively enhanced, and the rotation and translation invariance of the image can be guaranteed; finally, image recognition can be effectively realized, and the traditional complex network image recognition technology is solved. In the process of image modeling, the number of nodes is large, the amount of calculation is large, and the feature points cannot be extracted well, which has strong creativity.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to a complex network image recognition method based on the structural balance theory. Background technique [0002] Image recognition is one of the key research fields of artificial intelligence. Successfully applied in traffic monitoring, biomedical image recognition, handwriting recognition, automatic driving and other fields. At present, many valuable image recognition methods have been formed, such as region-based methods, texture-based image recognition methods, model-based image recognition methods, K-L-based image recognition methods, geometric feature-based image recognition methods, and edge contour-based image recognition methods. Image recognition methods, etc. However, the above methods are closely related to the position and order of pixels in the image, so their recognition effects are weakened to varying degrees in the face of image plane rotation invarian...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06V10/84
CPCG06F18/29
Inventor 马林妹王银河
Owner GUANGDONG UNIV OF TECH
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