Goal-Directed Search with a Top-Down Modulated Computational Attention System

In this paper we present VOCUS: a robust computational attention system for goal-directed search. A standard bottom-up architecture is extended by a top-down component, enabling the weighting of features depending on previously learned weights. The weights are derived from both target (excitation) and background properties (inhibition). A single system is used for bottom-up saliency computations, learning of feature weights, and goal-directed search. Detailed performance results for artificial and real-world images are presented, showing that a target is typically among the first 3 focused regions. VOCUS represents a robust and time-saving front-end for object recognition since by selecting regions of interest it significantly reduces the amount of data to be processed by a recognition system.