Wavelet transforms and neural networks applied to image retrieval

We face the problem of retrieving images from a database. During training a wavelet-based description of each image is first obtained using a Daubechies 4-wavelet transformation. Resulting coefficients are used to train a neural network (NN). During retrieval, a given image is presented to the already trained NN. The system responds with the most similar images. Three different ways to obtain the coefficients of the wavelet transform are tested: from the entire image, from the histogram of the biggest circular window inside the image color channels, and from the histograms of square sub-images in the image channels of the original image. 120 color images of airplanes were used for training and 240 for testing. The best efficiency of 88% was obtained with the third description method