Relational entropy-based saliency detection in images and videos

Salient regions in an image facilitate the non-uniform allocation of computational resources to just the interesting parts of an image. In this paper, we present a saliency detection mechanism using relational distributions that capture geometric statistics based on distance and gradient direction relationships between pixels. The entropy of these normalized distributions is related to saliency. We employ an efficient technique for calculating the Rényi entropy of the probabilistic relational distributions using Parzen window weighted samples, thus eliminating the need for constructing intermediate histogram representations. We quantitatively demonstrate the biological plausibility of our method by showing how the saliency maps produced strongly correlate to human fixations in still images and to dominant objects in video. We find that our approach is better than six other saliency models.