Selective Visual Attention: Computational Models and Applications

5.7 Top-down Modelling in the Bayesian Framework

In Section 3.7, a saliency model (SUN) with more comprehensive statistics is only considered for its bottom-up part. However, the framework of the model includes both bottom-up and top-down parts. For the sake of completeness, here we briefly present its top-down part in [19].

5.7.1 Review of Basic Framework

The SUN model assumes that the salient location is closely related to the probability of a target's presence at each location. The location with higher probability of the target's appearance has a larger salient value. Let z be a point in the visual field (or a pixel in the input image) and let the binary random variable C denote whether or not the point belongs to a target class, C {0, 1}. The random variable l and random vector f denote the location (or pixel coordinates) and the visual features of a point, respectively. The saliency of a point z in the visual field is directly proportional to the probability , where f_z represents the features observed at the location z (here f_z is written as a vector denoted by a bold letter) and l_z is the coordinate of z. The saliency at point z can be calculated using Bayes' rule as

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