Manifold Spectral Clustering Image Segmentation Algorithm Based on Local Geometry Features

To improve the accuracy and timeliness of spectral clustering image segmentation,an algorithm of manifold spectral clustering image segmentation based on local geometry features is proposed.Firstly,considering the manifold structure of image data,the relationship of data intrinsic dimensions is obtained by performing spectral clustering based on local principal components analysis in the k-nearest neighbor region of data points.Then,the local linear reconstruction technique in manifold learning is introduced,and the similarity of local tangent space between data is obtained via mixed linear analyzers,and the similarity matrix with local geometric features is constructed by merging the intrinsic dimension and the local tangent space.Nystr m technique is utilized to approximate eigenvectors of the image to be segmented,and spectral clustering is performed on the constructed k principal eigenvectors.Finally,experiments on Berkeley dataset show the advantages of the proposed algorithm in accuracy and timeliness.