This study applies graph spectra and probabilistic neural networks to the task of supervised images classification. At first

comers are extracted from images to construct Delaunay graphs. These relational graphs have proved alluring as structural representations for images. Then graph spectral decomposition method is adopted to get eigenvalues and eigenvectors of the adjacency matrix. Graph spectra can preserve the primary structure of graphs. The spectrum of these graphs will be used as feature vectors for classification. At last probabilistic neural networks will give the classification result according to the vectors . As for the classifier

PNN has high speed of learning because the learning rule is simple and new trainings patterns can be incorporated into a previously trained classifier quite easily

which might be important for a particular on-fine application. Experimental results show that this method can achieve the best result of images classification.