Abstract:

In order to solve the problem of nonhigh image recognition accuracy for traffic monitoring, an image classification was proposed based on radial basis function (RBF) neural network. Zernike array less noise sensitivity, shape features and good stability were considered to build a fourthorder array feature vector for feature extraction; and an adaptive fuzzy clustering method fuzzy Cmeans was used to solve hidden neurons uncertain of RBF neural network. The simulation analysis shows that the classifier has a higher recognition rate than the classifier based on fuzzy Cmeans clustering algorithm of
BP and RBF neural network, a lower computational complexity than RBF neural network classifier with particle swarm of fuzzy Cmeans clustering algorithm, though they have similar recognition rate. Simulation and experiments show that this method has better classification capabilities and higher computational efficiency.

Key words: Zernike moment, fuzzy Cmeans, radial basis function neural network, image classifier