Non-negative Orthogonal Matrix Factorization Based Multi-view Clustering Image Segmentation Algorithm
ZHANG Rongguo1, CAO Junhui1, HU Jing1, ZHANG Rui1, LIU Xiaojun2
1. College of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024; 2. School of Mechanical Engineering, Hefei University of Technology, Hefei 230009
Abstract：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.
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2023, Vol. 36 
