Visual Attention is Beyond One Single Saliency Map
This addresses the challenge of modeling dynamic visual attention for computer vision and psychology, representing an incremental improvement over existing bottom-up attention models.
The paper tackles the problem that visual attention cannot be captured by a single static saliency map, proposing a dynamic model in the frequency domain to predict human fixation distribution over time.
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.