Combining wavelet transforms and neural networks for image classification

A new approach for image classification based on the color information, shape and texture is presented. In this work, we use the three RGB bands of a color image in RGB model to extract the describing features. All the images in image database are divided into 6 parts. We use the Daubechies 4 wavelet transform and first order color moments to obtain the necessary information from each part of the image. The proposed image classification system is based on Back propagation neural network with one hidden layer. Color moments and wavelet decomposition coefficients from each part of the image are used as an input vector of neural network. 150 color images of aircrafts were used for training and 250 for testing. The best efficiency of 98% was obtained for training set, and 90% for the testing set.