Improvement of image classification with wavelet and independent component analysis (ICA) based on a structured neural network

Abstract

Image classification is a challenging problem in organizing a large image database. However, an effective method for such an objective is still under investigation. This paper presents a method based on wavelet and Independent Analysis Component (ICA) for image classification with adaptive processing of data structures. With wavelet, an image is decomposed into low frequency bands and high frequency bands. An image can be characterized by wavelet coefficients in the form of tree representation. While the histograms of low frequency wavelet components are effective in characterizing images, the histograms of high frequency wavelet components are similar for different images and therefore they cannot be directly used as features. We make use of ICA for feature extraction from high frequency bands to improve image classification. Two sets of features are used together to classify images using a structured neural network. In total, 2940 images generated from seven categories are used in experiments. Half of the images are used for training neural network and the other images used for testing. The classification rate of the training set is 92%, and the classification rate of the test set reaches 89%. The experimental results show effectiveness of the proposed method based on combined wavelet and ICA for image classification.