An Eye Fixation Database for Saliency Detection in Images

Abstract

To learn the preferential visual attention given by humans to specific image content, we present NUSEF- an eye fixation database compiled from a pool of 758 images and 75 subjects. Eye fixations are an excellent modality to learn semantics-driven human understanding of images, which is vastly different from feature-driven approaches employed by saliency computation algorithms. The database comprises fixation patterns acquired using an eye-tracker, as subjects free-viewed images corresponding to many semantic categories such as faces (human and mammal), nudes and actions ( look , read and shoot ). The consistent presence of fixation clusters around specific image regions confirms that visual attention is not subjective, but is directed towards salient objects and object-interactions. We then show how the fixation clusters can be exploited for enhancing image understanding, by using our eye fixation database in an active image segmentation application. Apart from proposing a mechanism to automatically determine characteristic fixation seeds for segmentation, we show that the use of fixation seeds generated from multiple fixation clusters on the salient object can lead to a 10% improvement in segmentation performance over the state-of-the-art.