"Feature-aligned segmentation using correlation clustering" by Yixin Zhuang, Hang Dou et al.

Article Title

Feature-aligned segmentation using correlation clustering

Authors

Yixin Zhuang, National Digital Switching System Engineering & Technological Research Center, Zhengzhou, 450001, China.
Hang Dou, Washington University in St. Louis, St. Louis, 63130, USA.
Nathan Carr, Adobe, San Francisco, 94103, USA.
Tao Ju, Washington University in St. Louis, St. Louis, 63130, USA.

Keywords

mesh segmentation, correlation clustering (CC), feature lines

Abstract

We present an algorithm for segmenting a mesh into patches whose boundaries are aligned with prominent ridge and valley lines of the shape. Our key insight is that this problem can be formulated as correlation clustering (CC), a graph partitioning problem originating from the data mining community. The formulation lends two unique advantages to our method over existing segmentation methods. First, since CC is non-parametric, our method has few parameters to tune. Second, as CC is governed by edge weights in the graph, our method offers users direct and local control over the segmentation result. Our technical contributions include the construction of the weighted graph on which CC is defined, a strategy for rapidly computing CC on this graph, and an interactive tool for editing the segmentation. Our experiments show that our method produces qualitatively better segmentations than existing methods on a wide range of inputs.