An Attentional System Combining Top-Down and Bottom-Up Influences

Attention plays an important role in human processing of sensory information as a mean of focusing resources towards the most important inputs at the moment. In vision it has been argued attentional processes are crucial for dealing with the complexity of real world scenes. The problem has often been posed in terms of visual search tasks. It has been shown that both the use of prior task and context information top-down influences and favoring information that stands out clearly in the visual field bottom-up influences can make such tasks more efficient. In generic scene analysis one presumably has a combination of these influences. In this paper we describe a computational model that performs such a combination in a principled way. The system learns an optimal representation of the influences of task and context and thereby constructs a biased saliency map representing the top-down information. This map is combined with bottom-up saliency maps in a process progressing over time as a function over the input. The system is applied to search tasks in single images as well as in real scenes, in the latter case using an active vision system capable of gaze shifting. The proposed model has the desired qualities and goes beyond earlier proposed systems.