基于非负正交矩阵分解的多视图聚类图像分割算法

Non-negative Orthogonal Matrix Factorization Based Multi-view Clustering Image Segmentation Algorithm

多视图聚类在应对非线性结构数据上具有一定优势,却存在需要后处理和时间效率较低等缺点.针对这一问题,文中提出基于非负正交矩阵分解的多视图聚类图像分割算法.首先,提取图像多视图数据,使用流形学习非线性降维方法获取每个视图的谱嵌入矩阵,构建相应的谱块结构.再设计自适应权值,将谱块结构融合成一致性图矩阵.然后,对一致性图矩阵进行非负正交矩阵分解,获取非负嵌入矩阵.最后,由非负嵌入矩阵获得多视图特征的聚类,进而得到图像分割结果.在5个数据集上的对比实验表明,文中算法在分割精度和时间效率上都有一定提升.

Multi-view graph clustering shows some advantages in dealing with nonlinear structured data,but it exhibits drawbacks such as the need of post-processing and low time efficiency.To solve this problem,a non-negative orthogonal matrix factorization based multi-view clustering image segmentation algorithm(NOMF-MVC)is proposed.Firstly,multi-view data of an image is extracted,and the manifold learning nonlinear dimensionality reduction method is employed to obtain the spectral embedding matrix of each view.Corresponding spectral block structure is constructed and it is fused into a consistency graph matrix via designed adaptive weights.Secondly,the non-negative embedding matrix is obtained by the non-negative orthogonal matrix factorization of the consistency graph matrix.Finally,the clustering of multi-view features is performed by the non-negative embedding matrix,and thereby image segmentation results are yielded.Comparative experiments on five datasets show certain improvements in segmentation accuracy and time efficiency achieved by NOMF-MVC.