基于图非负矩阵分解的图像配准

摘要

A novel image registration method is proposed based on non-negative matrix factorization by using the absolute feature vectors of the spectrum of graph.First,the non-negative weight matrices of the undirected weighted graph by the feature points of the images are constructed,from which the feature basis image obtained can contain the global features of the original images using non-negative matrix factorization.The initial value of non-negative matrix factorization is evaluated iteratively using the absolute feature vectors of the spectrum of the non-negative weight matrices,which can reflect the structural characteristics information of the graph,and improve the image matching rate.Finally,the correct feature correspondences are found in the feature basis vectors space between the weighted graphs.The experiments on both synthetic images and real-word images indicate that the proposed method is feasible for image registration.%本文提出一种新的利用图的谱对应绝对值特征向量的非负矩阵分解图像配准方法.首先利用图像特征构造了无向权图的非负权矩阵,通过非负矩阵分解得到了包含原始图像全部特征的特征基图像；然后将非负权矩阵谱对应绝对值特征向量作为非负矩阵分解的初始值进行迭代,既能反映图的结构特征信息,又能提高图像的匹配率；最后在特征基向量空间找到了两图的正确特征对应关系.合成图像和真实图像实验结果表明了本文图像配准方法的可行性.