Applications of independent component analysis to image feature extraction

Independent Component Analysis (ICA) is a new signal processing method developed recently which, analyzes the data from a-statistical point of view. In ICA, one can try to express a set of random variables as linear combinations of statistically independent components. In this paper, ICA is applied to image feature extraction, and the information maximization algorithm is performed to optimize the results. From the results, it can be seen that the extracted features represent the image data in a natural way. In addition, the ICA basis vectors are localized and oriented, and sensitive to lines and edges of varying thickness of images. As an application of these extracted features, another denoising experiment is done. In this experiment a Gaussian noise is reduced by applying a soft-thresholding operator on the extracted ICA coefficients.