Hierarchical-clustering of parametric data with application to the parametric eigenspace method

A novel method for hierarchical-clustering of parametric data is proposed (in this article, we assumed that these data were parameterized by a single parameter in multidimensional spaces). In the proposed clustering method, the continuity of a parameter is preserved in each class, and furthermore, the optimal loci of class boundaries and the appropriate number of classes are determined. To improve the recognition performance of the parametric eigenspace method, which is an object recognition method based on the visual learning approach, the proposed clustering method is applied to the construction of tree-structured dictionaries for this recognition method. Experimental result shows that these tree-structured dictionaries improve the recognition performance of the parametric eigenspace method without a decrease in recognition accuracy