A computational model of selecting visual attention based on bottom-up and top-down feature combination

Selecting attention is an important cognitive psychology concept originally which has received much attention from scholars in the field of computer science. Nowadays, selecting attention has much application in computer vision. Most current computational models of attention focus on bottom-up features and ignore scene information. In this paper, a model of selecting visual attention guidance based on both bottom-up and top-down features was proposed. We used two datasets to evaluate the performance of the model, and also compare ours with Itti's model, which is particularly famous for visual attention. Experiments indicate that our model is applicable to the simulation of visual attention, and it achieves better performance in attention transferring than the models existed.