Texture classification using hierarchical discriminant analysis

As the representative of the linear discriminant analysis, the Fisher method is most widely used in practice and it is very effective in two-class classification. However, when it is expanded to multi-class classification problem, the precision of its discrimination may become worse. One of the main reasons is an occurrence of overlapped distributions on a discriminant space built by Fisher criterion. In order to take such overlap among classes into consideration, our approach builds a new discriminant space with hierarchical tree structure for overlapped classes. In this paper, we propose a new hierarchical discriminant analysis for texture classification. We can divide a discriminant space into subspace by recursively grouping overlapped classes. In the experiment, texture images of many classes are classified based on the proposed method, and we show the outstanding result compared with the conventional method.