Selective Visual Attention: Computational Models and Applications

3.6 Discriminant Saliency Based on Centre–Surround

Discriminant analysis is a technique that has frequently been applied in pattern recognition. Its objective is to classify a set of observed data into predefined classes such that the expected error probability tends to be minimum. Discriminant analysis needs to build a discriminant function or criterion based on a set of observed data in which the classes are known (as the training set), and the discriminant function or criterion is used to predict the class of new observed data with unknown class. Sometimes this discriminant function is implemented to select features of recognized objects, determining which features can distinguish one class from all others. Since these observed data are often uncertain, the method is rooted in statistical signal processes such as signal detection and estimation mentioned in Section 2.6, with decision theory and information theory. Decision theory is to select or find the optimal decision scheme by a quantitative method according to the information criterion. A discriminant saliency (DISC) model is based on decision theory.

The first literature on DISC modelling is proposed by Gao et al. [39]. It is formulated as a recognition problem, that is to recognize the classes of some objects in a scene by selecting optimal features that most discriminate one class from the others. Optimal feature selection is just like saliency determination in the given object context. For instance, the shape and contour ...