利用广义信息熵谱选择的图像分割

doi:10.16451/j.cnki.issn1003-6059.201903004

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Abstract The clustering result is determined by the quality of the eigenvectors (spectral) of the related graph Laplacian in spectral clustering, and therefore eigenvector selection is crucial. To solve this problem, spectral discrimination(SD), spectral discrimination availability(SDA) and spectral discrimination degree(SDD) are defined based on generalized information entropy. SD is exploited to measure the clustering information of each eigenvector. SDA is utilized to remove the ineffective eigenvectors for clustering. SDD is employed to construct a selective clustering ensemble scheme based on contribution for clustering. Thus, a spectral clustering algorithm based on eigenvector selection is proposed. The experimental results on varied natural images show that the proposed algorithm is simple and effective.

Key words： Generalized Information Entropy Spectral Clustering Eigenvector Selection Clustering Ensemble Image Segmentation

Received: 16 April 2018

ZTFLH:

TP 391.4

Fund:Supported by National Natural Science Foundation of China(No. 61471003), Program in the Youth Elite Support Plan in Universities of Anhui Province(No.Higher Education in Anhui 2014(11)), Natural Science Foundation of Anhui Provincial Education Department(No.KJ2014A041, KJ2016A151, KJ2018A05

Corresponding Authors: ZHANG Daming, Ph.D., associate professor. His research interests include pattern recognition and image processing.

About author:: (ZHANG Xueyong, Ph.D., professor. His research interests include optical metrology and 3D computer vision.)
(LI Lu, master, associate professor. Her research interests include digital image proce-ssing.)
(LIU Huayong, master, professor. His research interests include computer graphics.)

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Cite this article:

ZHANG Daming,ZHANG Xueyong,LI Lu等. Image Segmentation Using Generalized Information Entropy for Eigenvector Selection[J]. , 2019, 32(3): 225-236.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201903004 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I3/225

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