融入局部几何特征的流形谱聚类图像分割  

Manifold Spectral Clustering Image Segmentation Algorithm Based on Local Geometry Features

作　　者：张荣国[1] 姚晓玲 赵建[1] 胡静[1] 刘小君[2] ZHANG Rongguo;YAO Xiaoling;ZHAO Jian;HU Jing;LIU Xiaojun(College of Computer Science and Technology,Taiyuan University of Science and Technology,Taiyuan 030024;School of Mechanical Engineering,Hefei University of Technology,Hefei 230009)

机构地区：[1]太原科技大学计算机科学与技术学院,太原030024 [2]合肥工业大学机械工程学院,合肥230009

出　　处：《模式识别与人工智能》2020年第4期313-324,共12页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.51875152);山西省自然科学基金项目(No.201801D121134)资助。

摘　　要：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.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.

关 键 词：相似性矩阵 本征维数 局部切空间 流形谱聚类 图像分割 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]