A visual attention model combining top-down and bottom-up mechanisms for salient object detection

Selective attention in the human visual system is performed as the way that humans focus on the most important parts when observing a visual scene. Many bottom-up computational models of visual attention have been devised to get the saliency map for an image, which are data-driven or task-independent. However, studies show that the task-driven or top-down mechanism also plays an important role during the formation of visual attention, especially with the cases of object detection and location. In this paper, we proposed a new computational visual attention model by combining bottom-up and top-down mechanisms for man-made object detection in scenes. This model shows that the statistical characteristics of orientation features can be used as top-down clues to help for determining the location for salient objects in natural scenes. Experiments confirm the effectiveness of this visual attention model.