Feature extraction using wavelet transform for neural network based image classification

In order to design an image classification or recognition scheme which should have a robustness in classification approaching as close as possible to that of the human biological recognition system, two factors must be taken into account: it must be able to automatically extract global properties of the images; and it must be able to filter out the variations such as scaling and rotation in the images. Wavelet transforms of the images with high frequency components truncated off seem to be able to meet both of these conditions. This is because low frequency components are spread in the time domain and can be treated as global property while high frequency components, concentrated in time domain, can be discarded. Information at different resolution scales provided by wavelet features lead to highly discriminating, robust classifiers. Wavelets can examine data at different scales and frequencies. The theory behind the wavelets and their suitability for classification is discussed. The authors discuss extraction and how the wavelet transform is implemented. Finally, results of feature extraction are given.