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Analyzing Differences between Gabor Functions and Ica Filters Learned from Natural Scenes

It is known that applying independent component analysis on natural scenes spans filters similar to real-valued Gabor functions. In this work, we investigate what are the actual differences between ICA filters and Gabor functions. Firstly, we assume a simple model where ICA filters can be represented as the convolution of a Gabor component with an unknown function. Then, after estimating the Gabor component, we analyze how the ICA filters and their Gabor components differ in energy distribution and individual coding behavior. We found that similar to V1 receptive fields, ICA filters compensate for power spectrum of the input signal. However, their Gabor components do not preserve this property due to differences in energy distribution. On the other hand, the analysis of coding behavior for individual of ICA filters and Gabor components shows that there are relative small differences when using objective measures of sparseness and reconstruction error.