Computer Science ›› 2020, Vol. 47 ›› Issue (6): 170-175.doi: 10.11896/jsjkx.190400052

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Block Integration Based Image Clustering Algorithm

LIU Shu-jun, WEI Lai   

  1. School of Information Engineering,Shanghai Maritime University,Shanghai 201306,China
  • Received:2019-04-09 Online:2020-06-15 Published:2020-06-10
  • About author:LIU Shu-jun,born in 1993,postgra-duate.Her main research interests include pattern recognition and machine learning.
    WEI Lai,born in 1980,Ph.D,associate professor,postgraduate supervisor.His main research interests include pattern recognition,machine learning and data mining.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61203240) and Shanghai Scientific Research and Innovation Project (14YZ102).

Abstract: Spectral based subspace clustering algorithms have shown good results.But the traditional subspace clustering algorithms need to vectorize the image,which will lead to the losses of the two-dimensional structure informations carried by the ima-ge itself.In order to reduce the losses,block integration based image clustering(BI-CI) algorithm is proposed.First,the images are divided into several matrix blocks.Then,the nuclear norm based matrix regression is used to get the coefficient matrix of one block,and a method is proposed to set the weight for each matrix block according to the rank information of matrix blocks.Finally,based on each coefficient matrix and according to the rank of the corresponding matrix block,the integral coefficient matrix is obtained.The final clustering results are obtained by using spectral clustering performed on the coefficient matrix.Experimental results show that the proposed method is more robust than the existing algorithms and can achieve more accurate clustering results.

Key words: Block, Matrix regression, Nuclear norm, Rank, Subspace clustering

CLC Number: 

  • TP391
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