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Active Object Recognition with a Space-Variant Retina

When independent component analysis (ICA) is applied to color natural images, the representation it learns has 
spatiochromatic properties similar to the responses of neurons in primary visual cortex. Existing models of ICA 
have only been applied to pixel patches. This does not take into account the space-variant nature of human vision. 
To address this, we use the space-variant log-polar transformation to acquire samples from color natural images, 
and then we apply ICA to the acquired samples. We analyze the spatiochromatic properties of the learned 
ICA filters. Qualitatively, the model matches the receptive field properties of neurons in primary visual cortex, including 
exhibiting the same opponent-color structure and a higher density of receptive fields in the foveal region 
compared to the periphery. We also adopt the “self-taught learning” paradigm from machine learning to assess the 
model’s efficacy at active object and face classification, and the model is competitive with the best approaches in computer vision.