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中文摘要：
提出了一种非负矩阵分解（non-negative matrix factorization，NMF）和邻接谱相结合的图像分类方法．该方法首先利用图像中的特征点构造邻接矩阵，然后使用邻接谱作为非负矩阵分解迭代规则的初始值，并将经过非负矩阵分解得到的基向量作为图像的分类样本，最后采用概率神经网络（probabilistic neural network，PNN）分类器对图像进行分类．模拟实验和真实实验的比较表明，该方法是可行和有效的，并且进一步提高了图像分类的准确率和稳定性．
英文摘要：
Combined non-negative matrix factorization （NMF） with adjacency spectra, a new method of image classification was proposed to extract characteristic information of an image. Firstly, the adjacency matrix was constructed by the feature points of the image. Secondly, the initial value of NMF iterative was evaluated by means of adjacency matrix, and then the samples of image classification were obtained through basis vectors of NMF. Finally, image classification was performed by adopting probabilistic neural networks （PNN） classifier. Experimental results of synthetic data and real images show that the method not only has feasibility and validity, but also further improves recognition rate and stability of image classification.
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