Abstract: A biologically-inspired model for the computer vision community was proposed. At first, a set of basis functions that accorded with visual responses to natural stimuli was learned by using eye-fixation patches from an eye-tracking dataset. Then image calculation model was established and features was derived based on the principle of sparse representation: including global continuity, regional color contrast, and local complexity contrast. And then refer to the principle that activity in cells responding to stimuli, a new feature combination theory was proposed to achieve features fusion. Afterwards, some experiments extracting regions of interest from typical scenes prove that this algorithm has superiontity than other algorithms, and the algorithm was applied in virtual and reality interactivity. It can effectively extract effective regions and eliminate virtual scene area.