A Visual Attention Based Object Detection Model beyond Top-Down and Bottom-up Mechanism

Abstract

Traditional saliency-based attention theory supposed that bottom-up and top-down factors combine to direct attentional behavior. This dichotomy fails to explain a growing number of cases in which neither bottom-up nor top-down can account for strong selection biases. Thus, the top-down versus bottom-up dichotomy is an inadequate taxonomy of attentional control. In our previous study, we presented a general computational framework for detecting task-oriented salient objects in images beyond top-down and bottom-up mechanism. It possesses three parts: selection history, current goal and physical salience. Selection history is integrated with current goal and physical salience to compose an integrative framework. In this extended version, our ameliorated model is applied to face and car detection and simulates task-dependent reasonable eye trajectories (visual scan paths). Experimental results demonstrate that selection history (reward) is shown to influence saccade trajectories. Our findings support the idea that attention and gaze can be directed voluntarily to regions of interest (by selection history and current goal) and can be captured by local features of an object that stand out from the background (by physical salience).