安徽大学学报（自然科学版）2011，Vol.35Issue(5)：63-67,5.
基于递增权函数的邻接矩阵与非负矩阵分解的图像分类方法
Image classfication using increasing weighting function of adjacency matrix of graph and non-negetive matrix factorization
摘要
Abstract
In this paper, the adjacency matrix of graph based on the increasing weighting function combined with the method of non-negative matrix factorization was applied to the image classification. First, the character points could be distilled from different images. Then, these points were used to construct the adjacency matrix of the increasing weighting function, and the eigenvector of the image could be obtained by the non - negative factorization of the adjacency matrix. Finally, the eigenvector was put into PNN ( Probabilistic Neural Network) classifier to accomplish the image classification. Several groups of experiments were presented between simulating images and real images. The results showed that the method presented in this paper was feasible and accurate.关键词
递增权函数/邻接矩阵/非负矩阵/图像分类Key words
increasing weighting function/ adjacency matrix/ non - negative matrix factorization/ image classification分类
信息技术与安全科学引用本文复制引用
蒋云志,王年,汪斌..基于递增权函数的邻接矩阵与非负矩阵分解的图像分类方法[J].安徽大学学报（自然科学版）,2011,35(5):63-67,5.基金项目
国家自然科学基金资助项目(60772121) （60772121）
安徽省高校青年教师基金资助项目(2008JQl023) （2008JQl023）
安徽省教育厅自然科学研究基金重点资助项目(KJ2010A007) （KJ2010A007）
