Top-Down Visual Attention with Complex Templates

Abstract

Visual attention can support autonomous robots in visual tasks by assigning resources to relevant portions of an image. In this biologically inspired concept, conspicuous elements of the image are typically determined with regard to different features such as color, intensity or orientation. The assessment of human visual attention suggests that these bottom-up processes are complemented -and in many cases overruled -by top-down influences that modulate the attentional focus with respect to the current task or a priori knowledge. In artificial attention, one branch of research investigates visual search for a given object within a scene by the use of top-down attention. Current models require extensive training for a specific target or are limited to very simple templates. Here we propose a multi-region template model that can direct the attentional focus with respect to complex target appearances without any training. The template can be adaptively adjusted to compensate gradual changes of the object's appearance. Furthermore, the model is integrated with the framework of region-based attention and can be combined with bottom-up saliency mechanisms. Our experimental results show that the proposed method outperforms an approach that uses single-region templates and performs equally well as state-of-the-art feature fusion approaches that require extensive training.