Importance Driven Contour Tree Simplification

Real-world data sets produce unmanageably large contour trees because of noise and artifacts. It makes the contour tree impractical in data analysis and visualization. This paper proposes an importance-driven contour tree simplification approach which combines different measures of importance through an importance triangle to maximize advantages of each measure of importance. Extended Gaussian image, map projection, and K-Means clustering are used to manipulate importance measure vectors, which makes the simplification more meaningful and efficient. The proposed approach can be generalized to process branches with more than three measures.