A similarity evaluation method for volume data sets by using critical point graph

The ever increasing use of computer simulation has proportionately increased the demands for an efficient method for classification of a large amount of computational results or for searching an arbitrary data set in a given database. In order to classify or to search for a computational simulation result, it is necessary to evaluate the similarity between a given data in respect to the reference data in a database. A similarity estimation method which employs "Critical Point Graph (CPG)" as an index has proven effective, however this method does not support transformation operations such as rotation or scaling. In this paper, we propose a CPG-based similarity estimation method supporting both rotation and scaling transformations for two and three dimensional scalar data sets (volume data sets). We could confirm its effectiveness, and also proved superior to the traditional Contour Tree (CT) based matching technique which uses affine-invariant metrics. Some discussion about the proper use of these matching techniques is also presented to clarify the advantages and disadvantages.