Center-surround divergence of feature statistics for salient object detection

In this paper, we introduce a new method to detect salient objects in images. The approach is based on the standard structure of cognitive visual attention models, but realizes the computation of saliency in each feature dimension in an information-theoretic way. The method allows a consistent computation of all feature channels and a well-founded fusion of these channels to a saliency map. Our framework enables the computation of arbitrarily scaled features and local center-surround pairs in an efficient manner. We show that our approach outperforms eight state-of-the-art saliency detectors in terms of precision and recall.