Learning Appearance Based Models: Mixtures of Second Moment Experts

This paper describes a new technique for object recognition based on learning appearance models. The image is decomposed into local regions which are described by a new texture representation called “Generalized Second Moments” that are derived from the output of multiscale, multiorientation filter banks. Class-characteristic local texture features and their global composition is learned by a hierarchical mixture of experts architecture (Jordan & Jacobs). The technique is applied to a vehicle database consisting of 5 general car categories (Sedan, Van with back-doors, Van without back-doors, old Sedan, and Volkswagen Bug). This is a difficult problem with considerable in-class variation. The new technique has a 6:5% misclassification rate, compared to eigen-images which give 17:4% misclassification rate, and nearest neighbors which give 15:7% misclassification rate.