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Selective Visual Attention: Computational Models and Applications by Weisi Lin, Liming Zhang

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3.7 Saliency Using More Comprehensive Statistics

Let us review the basic problem raised in the earlier part of this book: what is the attentional location in the visual field? There are many different points of view. Some models simulate the biological visual system (e.g., the BS model and its variations), and some are mainly based on statistical signal processing theory such as information theory (the AIM model) and decision theory (the DISC model), although biological plausibility is also appreciably considered. A model of saliency using natural statistics abbreviated as SUN by taking the first letter of ‘saliency using natural’ statistics [9, 50] belongs to the second type, that is based on information theory in a Bayesian framework. It is somewhat similar to the AIM model while working in a pure bottom-up fashion, but the statistics of the SUN model are based on whole lifetime in the world; that is, the statistics have to span all time and space, not for some data sets or for the current period as with the AIM model. Consequently, the SUN model is more comprehensive for probability estimation that is independent of the test image. In addition, the standpoint of the SUN model is to search the object's position, and hence both top-down and bottom-up attentions in the statistical model are considered in an integrated manner.

Its idea is very basic: the goal of development for the HVS is to find potential targets (food and prey) for primitive humans' or animals' survival [50]. ...

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