Image clustering method based on self-representation and graph constraint non-negative matrix factorization

A non-negative matrix decomposition, image clustering technology, applied in character and pattern recognition, complex mathematical operations, instruments, etc., can solve problems such as the impact of accuracy

Active Publication Date: 2020-05-22
BEIJING UNIV OF TECH
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Problems solved by technology

If you continue to use GNMF to classify images, the accuracy will be affected

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  • Image clustering method based on self-representation and graph constraint non-negative matrix factorization
  • Image clustering method based on self-representation and graph constraint non-negative matrix factorization
  • Image clustering method based on self-representation and graph constraint non-negative matrix factorization

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[0065] Below in conjunction with accompanying drawing and experiment the technical method of this invention is further described.

[0066] Based on the present invention, an image clustering method based on non-negative matrix factorization of self-representation and graph constraints is proposed, referring to figure 1 , the specific implementation includes:

[0067] A. Use the graph normalized NMF algorithm (GNMF) and low-rank embedding (LRE) to input the original image data set X=[x 1 , x 2 ,...,x N ] to build an analytical model, where each x i Is an image matrix, pulled into a column vector here, the size is

[0068] B. Solve the model by using the alternate iteration method, perform non-negative matrix decomposition on the input non-negative data, and obtain the low-dimensional representation matrix of the image.

[0069] C. According to the obtained low-dimensional representation matrix V, cluster the original image.

[0070] Further, the step A is specifically: ...

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Abstract

The invention relates to an image clustering method based on self-representation and graph constraint non-negative matrix factorization, and is particularly suitable for clustering of complex categories in a data set. According to the method, dimensionality reduction is carried out on high-dimensional data based on an image clustering method of non-negative matrix factorization of self-representation and graph constraint, and particularly for the situation that an abnormal value exists in an image, the abnormal value being recorded as LRE-GNMF. A target function is solved by using an alternating iteration method to obtain a low-dimensional representation coefficient matrix; and the images are clustered by using the low-dimensional representation coefficient matrix. According to the method,a non-negative data matrix is used as input, and low-rank embedding (LRE) is adopted, so that data close to each other in a high-dimensional space can still be kept close to each other in a learned low-dimensional space, and therefore, the local structure of the data can be kept. The method can be widely applied to the field of image recognition.

Description

technical field [0001] The invention is a matrix decomposition method for machine learning, in particular to an image clustering method based on self-expression and map constraint non-negative matrix decomposition, and is especially suitable for clustering with complex categories in data sets. Background technique [0002] High-dimensional data is ubiquitous in modern computer vision and image processing research. However, high-dimensional data will not only increase storage overhead and computational complexity, but also reduce the effectiveness of algorithms in practical applications. Often, it is necessary to find a data representation that reveals the underlying data structure in high-dimensional data, which often facilitates further data processing. Therefore, in order to find suitable data representations, researchers have developed methods such as image reconstruction, image clustering, matrix completion, etc. Among them, matrix decomposition technology is widely us...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06F17/16
CPCG06F17/16G06F18/23Y02T10/40
Inventor 孙艳丰尹帅胡永利
Owner BEIJING UNIV OF TECH
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