Even if the extraction of salient and useful information, i.e. observation, is an elementary task for human and animals, its simulation is still an open problem in computer vision. I define a process to derive specific and optimal laws to extract visual information and by the way model information without any constraints or a priori. Starting from salience definition and measure through the prism of information theory, I have developed an ecological inspired approach to model visual information extraction. I theoretically demonstrate some results previously presented, for instance, in spites of being fast and highly configurable, my model is as plausible as existing models designed for high biological fidelity, or that it proposes an adjustable trade-off between nondeterministic attentional behavior and properties of stability, reproducibility and reactiveness. We proposed to position our model in a benchmark data set containing 300 natural images with eye tracking data from 39 observers.

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