Combining segmentation and attention: a new foveal attention model

Abstract

Artificial vision systems cannot process all the information that they receive from the world in real time because it is highly expensive and inefficient in terms of computational cost. Inspired by biological perception systems, artificial attention models pursuit to select only the relevant part of the scene. On human vision, it is also well established that these units of attention are not merely spatial but closely related to perceptual objects (proto-objects). This implies a strong bidirectional relationship between segmentation and attention processes. While the segmentation process is the responsible to extract the proto-objects from the scene, attention can guide segmentation, arising the concept of foveal attention. When the focus of attention is deployed from one visual unit to another, the rest of the scene is perceived but at a lower resolution that the focused object. The result is a multi-resolution visual perception in which the fovea, a dimple on the central retina, provides the highest resolution vision. In this paper, a bottom-up foveal attention model is presented. In this model the input image is a foveal image represented using a Cartesian Foveal Geometry (CFG), which encodes the field of view of the sensor as a fovea (placed in the focus of attention) surrounded by a set of concentric rings with decreasing resolution. Then multi-resolution perceptual segmentation is performed by building a foveal polygon using the Bounded Irregular Pyramid (BIP). Bottom-up attention is enclosed in the same structure, allowing to set the fovea over the most salient image proto-object. Saliency is computed as a linear combination of multiple low level features such as color and intensity contrast, symmetry, orientation and roundness. Obtained results from natural images show that the performance of the combination of hierarchical foveal segmentation and saliency estimation is good in terms of accuracy and speed