Similarity-driven multi-level partial contour tree simplification

A contour tree is a topological abstraction of a scalar field. Contour tree simplification (CTS) removes branches corresponding to noise, while making the size of the tree small enough for maintaining essential structure of data. This paper proposes a similarity-driven multi-level partial CTS (PCTS) approach. The PCTS preserves branches corresponding to structures of interest or specific objects specified by users, while removing other branches of the contour tree. A clustering method (e.g. k-means clustering) is used to cluster branch nodes into groups based on their similarities (i.e., similar locations) in the attribute space. As a result, the contour tree is simplified with multi-levels based on different clustering groups. Furthermore, various interfaces and rendering windows are provided and synchronized, which makes the simplification process more meaningful and efficient compared with traditional simplification methods using slide-bar based approaches. The proposed approach can be generalized to process branches with more than three measures.