  Of later years, numerous bottom-up attention models have been proposed on
different assumptions. However, the produced saliency maps may be different
from each other even from the same input image. We also observe that human
fixation map varies across time greatly. When people freely view an image, they
tend to allocate attention at salient regions of large scale at first, and then
search more and more detailed regions. In this paper, we argue that, for one
input image visual attention cannot be described by only one single saliency
map, and this mechanism should be modeled as a dynamic process. Under the
frequency domain paradigm, we proposed a global inhibition model to mimic this
process by suppressing the {\it non-saliency} in the input image; we also show
that the dynamic process is influenced by one parameter in the frequency
domain. Experiments illustrate that the proposed model is capable of predicting
human dynamic fixation distribution.
