Entropy sequencing-based semi-supervision spectral clustering method for determining clustering number

Abstract

The invention discloses an entropy sequencing-based semi-supervision spectral clustering method for determining a clustering number, and mainly solves the problem of selection of a characteristic vector of a Laplacian matrix in spectral clustering. The method comprises the following steps of: performing permutation on the characteristic vector by an entropy sequencing theory so as to acquire an array of the characteristic vector with the uppermost importance; for a k class problem, extracting the first k arrays of the characteristic vector and projecting the arrays into a k-dimensional space; clustering according to the distance of each point and 2k semiaxes in the k-dimensional space; recording the preserved clustering number as c except for a class without points in a 2k class or a clustering class with the point number less than one percent of that of input data; extracting the first c arrays of the characteristic vector and circulating the operation until the clustering number is stable, wherein corresponding class number is the optimal clustering number; and marking an input data point according to the coordinate of each input point so as to acquire a clustering result. The method has the advantages of high self-adaption performance and high clustering precision rate and can be used for self-adaptively determining an image category number.

Description

technical field [0001] The invention belongs to the technical field of image processing and relates to an image clustering method, which can be applied to the field of image clustering to determine the number of clusters adaptively. Background technique [0002] Image clustering is an important step in image processing. The purpose of image clustering is to cluster different regions on the image into different classes according to the relationship between image pixels. Spectral clustering is an emerging clustering method in recent years. The idea of ​​this algorithm originated from the theory of spectral graph partitioning, and it is regarded as a multi-way partitioning problem of an undirected graph. The reason why spectral clustering is superior to traditional clustering algorithms is that it is not limited by the shape of the sample space and converges to the global optimal solution. Therefore, spectral clustering algorithms have been widely used in the field of image cl...

AI Technical Summary

Problems solved by technology

The algorithm has achieved better results to a certain extent, but the results for complex data are not ideal.

[0009] Since the above methods of automatically determining the number of clusters all select the eigenvectors corresponding to the top k largest eigenvalues, they have the following disadvantages: 1. Susceptible to noise and cause clustering errors; 2. The results are not good or invalid for large data; 3. .Need to manually set the threshold

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