A context-based model of attention (extended abstract) 1

It is well known that natural visual systems rely on attentional mechanisms that select and process relevant objects in an efficient way. Similarly, artificial visual systems need attentional-selection mechanisms to reduce the computational burden of processing entire images. So, their aim is to focus on the parts containing the object of interest. In the domain of natural vision the locus of selection has been debated for many years (see [1] for an overview). The two extreme views are (1) that selection takes place at an early stage of visual processing (i.e., early selection), and (2) that it takes place at a late stage (i.e., late selection). In early selection, attention is guided by conspicuous changes in elementary features, such as colour, texture, or spatial frequency. Models of early selection contain so-called saliency maps that contain the response respond to conspicuous changes in a single feature, e.g., [4]. The activities in these maps represent locations to be attended. In late selection, attention is guided by complex feature combinations or even objects [7]. Models of late selection rely on object templates that are matched to the contents of images [6]. From a computational point of view, both early and late selection pose considerabl