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Image spectral clustering method based on quick selection of landmark points

A landmark point and spectral clustering technology, applied in instruments, character and pattern recognition, computer parts and other directions, can solve the problems of image data distribution information loss, large error in image sparse representation, large amount of computation and storage, etc. Improve processing speed, improve accuracy, overcome the effect of noise

Active Publication Date: 2018-01-12
XIDIAN UNIV
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Problems solved by technology

The disadvantage of this method is that when performing spectral clustering on SAR images, only the number of input image features is reduced, and the amount of calculation and storage of this method is still very large.
The disadvantage of this method is that when the image is selected as a landmark point for spectral clustering, the selected landmark point is affected by the image data structure, resulting in the loss of image data distribution information, and the sparse representation of the image has a large error

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  • Image spectral clustering method based on quick selection of landmark points
  • Image spectral clustering method based on quick selection of landmark points
  • Image spectral clustering method based on quick selection of landmark points

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Embodiment Construction

[0038] The present invention will be further described below in conjunction with the accompanying drawings.

[0039] Refer to attached figure 1 , the concrete steps of the present invention are as follows.

[0040] Step 1, read all the images to be spectrally clustered.

[0041] Step 2, calculate the nearest neighbor graph of the image to be spectrally clustered.

[0042] Using Silverman's rule of thumb, calculate the bandwidth of the radial basis kernel function for all neighbor graphs of all images read.

[0043] The described specific steps for utilizing Silverman's rule of thumb are as follows:

[0044] In the first step, calculate the standard deviation of all images read in the same feature dimension according to the following formula:

[0045]

[0046] Among them, σ h Indicates the standard deviation of all images read in the hth feature dimension, Represents the square root operation, N represents the total number of all images read, ∑ represents the summation ...

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Abstract

The invention discloses an image spectral clustering method based on quick selection of landmark points and mainly aims to solve the problems that existing image spectral clustering methods are low inclustering precision and high in calculation complexity. The method comprises the steps that (1) all images to be subjected to spectral clustering are read; (2) neighbor images of the images to be subjected to spectral clustering are calculated; (3) the landmark points are selected; (4) a feature sparse representation matrix of the images to be subjected to spectral clustering is calculated; (5)a relevant matrix of the images to be subjected to spectral clustering is calculated; (6) a right singular feature matrix of the sparse representation matrix is calculated; and (7) recognition clustering is performed. Compared with some existing image spectral clustering technologies, through the method, a sparse representation error of the images can be lowered, the accuracy of a spectral clustering result is improved, and the calculation complexity is low.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to an image spectrum clustering method based on fast selection of landmark points in the technical field of image clustering. The invention can be used for automatic clustering of unlabeled images such as handwritten digital images and handwritten English letter images. Background technique [0002] Cluster analysis is an important method in machine learning and pattern recognition, and it is an effective means for people to understand and explore the inner relationship between things. It requires a reasonable classification according to the characteristics of the samples, so that objects in the same cluster have a high degree of similarity, and objects in different clusters are quite different. Traditional clustering algorithms such as K-means algorithm and EM algorithm are all based on convex spherical sample space. When the sample space is not convex, the algorit...

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Application Information

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IPC IPC(8): G06K9/62
Inventor 姬红兵王益新张文博刘龙王厚华陈爽月张海涛苏镇镇
Owner XIDIAN UNIV
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