Adaptive spectral clustering algorithm based on Nystr m method with multi-level structure in LUV color space
LUV色彩空间中多层次化结构Nystr m 方法的自适应谱聚类算法

In this paper,we propose an adaptive spectral clustering algorithm based on the Nystr m method with multi-level structures in LUV color space.First,we introduce the LUV color space,which can effectively avoid the influence of barely noticeable differences on the segmentation results,achieving better result in texture and edge regions.Second,we combine the spectral clustering algorithm based on multi-level structure and the Nystr m method.Our approach can reduce the operation time and solve the problem of memory overflow.Finally,in K-means,through the analysis of the eigengap to adaptive select the value of K,this approach can automatically determine the number of clusters.The proposed method is applied to image segmentation,respectively,in LUV color space and RGB color space.The experimental results show that in LUV color space we can obtain even better results.The data computation and operation time as well as the segmentation result of the proposed algorithm are superior,compared to the spectral clustering algorithm based on the Nystr m method (SC-N).