Information theoretic measure for visual target distinctness

It is of great benefit to have advance knowledge of human visual target acquisition performance for targets or other relevant objects. However, search performance inherently shows a large variance and depends strongly on prior knowledge of the perceived scene. A typical search experiment therefore requires a large number of observers to obtain statistically reliable data. Moreover, measuring target acquisition performance in field situations is usually impractical and often very costly or even dangerous. This paper presents a new method for characterizing information of a target relative to its background. The resultant computational measures are then applied to quantify the visual distinctness of targets in complex natural backgrounds from digital imagery. A generalization of the Kullback-Leibler joint information gain of various random variables is shown to correlate strongly with visual target distinctness as estimated by human observers. Bootstrap methods for assessing statistical accuracy were used to produce this inference.