Complex discriminant features for object classification

A new algorithm for the design of complex features, to be used in the discriminant saliency approach to object classification, is presented. The algorithm consists of sequential rotations of an initial basis of simple features, so as to maximize the discriminant power of the feature set for image classification. Discrimination is measured in an information theoretic sense. The proposed algorithm has lower complexity than popular techniques for learning parts, and is evaluated on classification tasks from the PASCAL challenge. It is shown that complex features consistently outperform simple features.