Information theoretic preattentive saliency: A closed-form solution

Employing an information theoretic operational definition of bottom-up attention from the field of computational visual perception, a very general expression for saliency is provided. As opposed to many of the current approaches to determining a saliency map, there is no need for an explicit, data-driven density estimation. Given the features, descriptors, or filter bank that one wants to use to describe the image content at every position, we provide a closed-form expression for the associated saliency at that location. This, indeed, makes explicit that what is considered salient depends on how, i.e., by means of which features, image information is described. We illustrate our result by determining a few specific saliency maps based on particular choices of features. One of them makes the link with the mapping underlying well-known Harris interest points, which is a result recently obtained in isolation. Another choice of features is, rather loosely, inspired by the success of histogram of oriented gradient descriptors and proves to provide state-of-the-art results on a collaborative benchmark for region of interest detection.