Discriminant analysis for perceptionally comparable classes
Traditional discriminate analysis treats all the involved classes equally in the computation of the between-class scatter matrix. However, we find that for many vision tasks, the classes to be processed are not equal in perception, i.e. a distance metric can be defined between the classes. Typical examples include head pose classification and age estimation. Aiming at this category of classification problem, this paper proposes a novel discriminant analysis method, called Class Distance based Discriminant Analysis (CDDA). In CDDA, the perceptional distance between two classes is exploited to weight the outer product in the between-class scatter computation, to concentrate more on the classes difficult to separate. Another novelty of CDDA is that to preserve the within-class local structure of multimodal labeled data, the within-class scatter is re-defined by complementing the similarity of the samples pairs in the nearby classes. The method is then applied to head pose classification and age estimation problem, and experimental results demonstrate the effectiveness of CDDA.