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