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

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3.8 Saliency Based on Bayesian Surprise

Surprise events or targets frequently occur in our life, and they often attract human attention or create a deep impression in the human brain. For instance, in films or television plays, the audience will pay more attention to the unexpected sequel after long suspense or fix their eyes on surprise locations or scenes. Surprise is out-of-expectation stimuli or events, which connotes uncertainty. If all things are deterministic, there is no surprise in the world, and this will result in loss of highlights and delight. Surprise may be also subjective; for the same visual data, the surprise degree of different individuals is different. Sometimes surprise represents novelty in temporal or spatial scope, which is related to saliency, because experiments on many video clips showed that for about 72% of all audiences their gaze shifts directly towards more surprising locations than the average in thousands of frames [10, 54].

But how do you quantify surprise? Itti and Baldi proposed a Bayesian definition of surprise in [10, 54, 55], called Bayesian surprise theory. In the theory, prior probability density is assigned over the possible models, and when the new stimuli arrive, the probability over possible models is updated by the Bayesian rule. The surprise is measured by KL divergence between the prior probability density and the posterior probability density. This definition of KL distance or divergence was mentioned in Sections 2.6.2 and 3.6.1. ...

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