关键词: 谱聚类, 空间临近信息, 相似性矩阵, Nystrom逼近策略, 近邻传播聚类算法

Abstract: Aiming at the default that when the traditional spectral clustering algorithm is applied to?image segmentation, it only uses the feature similarity information to construct similarity matrix and ignores the spatial adjacency information defect of spatial distribution of pixels, this paper presents a new similarity measure formula—weighted euclidean distance of the Gaussian kernel function, making full use of image feature similarity information and spatial adjacency information to structure similarity matrix. In the spectral mapping process, using Nystrom approximation strategy to approximate similarity matrix and eigenvectors, it greatly reduces the computational complexity to solve similarity matrix and reduces the memory consumption. This paper applies a new clustering algorithm—Affinity?Propagation to the low-dimensional subspace. It avoids the defect that traditional spectral clustering using K-means algorithm can not automatically determine the number of clusters and it is sensitive to initial value and easy to fall into local optimum. The experiments prove that the proposed algorithm obtains better segmentation results than the traditional spectral clustering algorithm.

Key words: spectral clustering, spatial adjacency information, similarity matrix, Nystrom approximation, Affinity Propagation（AP） algorithm