Image Segmentation Using Generalized Integrated Squared Error-Based Eigenvector Selection
ZHANG Da-Ming1,2, FU Mao-Sheng1, LUO Bin1
1.School of Computer Science and Technology, Anhui University, Hefei 230039 2.Department of Mathematics Physics, Anhui Institute of Architecture Industry, Hefei, 230022
Abstract Not all of the top eigenvectors contain clustering information for the task of real-world data clustering. Since the noise exists, the distribution of elements of an eigenvector is complex and it is necessary to select eigenvectors for spectral clustering. In this paper, the integrated squared error (ISE) divergence is generalized and the proposed generalized integrated squared error (GISE) is used to estimate the multimodality of data distribution and measure the clustering information of eigenvector. Then, a spectral clustering algorithm based on eigenvector selection is proposed. The experimental results on varied natural images segmentation show that the proposed algorithm is simpler and more effective than pervious algorithms.
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2011, Vol. 24 
