Image recognition based on wavelet invariant moments and wavelet neural networks

A novel image recognition system is constructed by combining Wavelet invariant moments with Wavelet neural networks in this paper. Firstly, global and local features of the image can be obtained by using Wavelet invariant moments. Secondly, the invariant features are fed into Wavelet neural networks. Finally, supervised invariant pattern recognition can be achieved by utilizing three characters of Wavelet neural networks, which are the automatic ascertaining the number of hidden layer unit, converging rapidly and never running into the partial minimum of networks. The experiment results demonstrate that using Wavelet invariant moments and Wavelet neural networks can achieve higher accuracy of image classification than the algorithm based on normal invariant moments and BP neural networks.