In this study, a bio-inspired approach for extracting efficient features prior to the recognition of scenes is proposed. It is highly inspired from the model of the mammals visual system. The retina contains many levels of neurons (bipolar, amacrine, horizontal and ganglion cells) accurately organized from cones and rods to the optic nerve up till the lateral geniculate nucleus (LGN) which is the main thalamic relay for inputs to the visual cortex. This structure probably eases other brain areas tasks in preprocessing the visual information. This paper is focusing on the study of these specific structures, relying on a bottom up approach to propose a comprehensive mathematical model of the low level image processing performed within the eye. The presented system takes into account the foveolar structure of the retina to produce a low-resolution representation of observed images by decomposing them into a local summation of elementary gaussian color histograms. This representation corresponds to the LGN biological organization. It has been thought that due to short timings, some very quick localization tasks involving particularly fast information processing pathways cannot be provided by the classical ones passing through higher level cortical areas. This work proposes a model of retinal coding and LGN-visual representation that we show provides reliable and sufficient early features for scenes recognition and localization. Experiments on real scenes using the developed model are presented showing the efficiency of the approach on localization.