融入局部几何特征的流形谱聚类图像分割

为了改善谱聚类图像分割的精准性和时效性,文中提出融入局部几何特征的流形谱聚类图像分割算法.首先,考虑图像数据的流形结构,在数据点的K近邻域内执行局部PCA,得到数据间本征维数的关系.然后,引入流形学习中的局部线性重构技术,通过混合线性分析器得到数据间局部切空间的相似性,结合二者构造含有局部几何特征的相似性矩阵.再利用Nystr m技术逼近待分割图像的特征向量,对构造的k个主特征向量执行谱聚类.最后,在Berkeley数据集上的对比实验验证文中算法的准确性和时效性优势.

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. Nyström 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.