A Bayesian approach to predicting the perceived interest of objects

This paper presents an algorithm designed to compute the perceived interest of objects in images. We measured likelihood functions via a psychophysical experiment in which subjects rated the perceived visual interest of 562 objects in 150 images. These results were then used to determine the likelihood of perceived interest given various factors such as location, contrast, color, and edge-strength. These likelihood functions are used as part of a Bayesian formulation in which perceived interest is inferred based on the factors. Our results demonstrate that our algorithm can perform well in predicting perceived interest.