On the discriminability of hROIs, human visually selected regions-of-interest

Many studies have tried to answer an important question: is it possible to predict human visually selected regions-of-interest (hROIs)? hROIs are defined as the loci of eye fixations and they can be analyzed by their spatial distribution over the visual stimulus and their temporal ordering. We used a simplified set of geometrical spatial kernels and linear filter models as bottom-up conspicuity operators that produce algorithmically selected regions-of-interest, aROIs. As a direct approach we measured the ability of these aROIs to predict human scanpaths. The level of prediction is measured by two similarity indices: Sp for spatial similarity and Ss for temporal ordering similarity. At the same time we assessed the discriminability of the hROI loci, in terms of conspicuity, with respect to non-selected (not of interest) regions of an image. We prove that this discrimination is possible and further correlates with the positional similarity index Sp. Other human scanpath experimental conditions are presented in parsing diagrams and discussed. A general top–down/bottom–up scanpath model is finally formulated.