Combining bottom-up and top-down attentional influences

Visual attention to salient and relevant scene regions is crucial for an animal’s survival in the natural world. It is guided by a complex interplay of at least two factors – image-driven, bottom-up salience [1] and knowledge-driven, top-down guidance [2, 3]. For instance, a ripe red fruit among green leaves captures visual attention due to its bottom-up salience, while a non-salient camouflaged predator is detected through top-down guidance to known predator locations and features. Although both bottom-up and top-down factors are important for guiding visual attention, most existing models and theories are either purely top-down [4] or bottom-up [5, 6]. Here, we present a combined model of bottom-up and top-down visual attention. Our proposed model first computes the naive, bottom-up salience of every scene location for different local visual features (e.g., different colors, orientations and intensities) at multiple spatial scales in a manner described in [6]. Next, the top-down component uses learnt statistical knowledge of the local features of the target and distracting clutter, to optimize the relative weights of the bottom-up maps such that the overall salience of the target is maximized relative to the surrounding clutter. Such optimization renders the target more salient than the distractors, thereby maximizing target detection speed [7]. Finding the optimal top-down weights that maximize the target’s salience relative to