Learning high-level independent components of images through a spectral representation

Statistical methods, such as independent component analysis, have been successful in learning local low-level features from natural image data. Here we extend these methods for learning high-level representations of whole images or scenes. We show empirically that independent component analysis is able to capture some intuitive natural image categories when applied on histograms of outputs of ordinary Gabor-like filters. This can be taken as an indication that maximizing the independence or sparseness of features may be a meaningful strategy even on higher levels of image processing, for such advanced functionality as object recognition or image retrieval from databases.