Feature-aligned segmentation using correlation clustering

Yixin Zhuang, Hang Dou, Nathan Carr, Tao Ju

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


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.

Original languageEnglish
Pages (from-to)147-160
Number of pages14
JournalComputational Visual Media
Issue number2
StatePublished - Jun 1 2017


  • correlation clustering (CC)
  • feature lines
  • mesh segmentation


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