Guiding a bottom-up visual attention mechanism to locate specific image regions using a distributed genetic optimization

Abstract

The purpose of this paper is to present an approach to locate specific regions in images. The novelty of the approach is the combination of a weighted bottom-up visual attention mechanism with a genetic algorithm optimization running on a computational grid. The visual attention mechanism is based on the model proposed by Itti and Koch [1]. A saliency map indicates the most interesting points in an image using a number of intermediate low level features, which are detected at different scales and orientations. Using the saliency map weights as parameters, the optimization problem is to minimize the number of most salient points needed to locate a set of reference image regions, previously (and manually) labeled as being interesting. Both an objective and subjective evaluation have demonstrated that the proposed approach is more effective when compared to a fixed weight attention mechanism. © Springer-Verlag Berlin Heidelberg 2006.