Computational Visual Media

, Volume 3, Issue 2, pp 147–160 | Cite as

Feature-aligned segmentation using correlation clustering

Open Access
Research Article

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.

Keywords

mesh segmentation correlation clustering (CC) feature lines 

Notes

Acknowledgements

We thank Dongming Yan for providing the code from Ref. [5] for comparison. The models in this paper were obtained from AIM@SHAPE and Princeton Segmentation Benchmark. The work was supported in part by a gift from Adobe System, Inc.

Supplementary material

41095_2016_71_MOESM1_ESM.wmv (70.7 mb)
Supplementary material, approximately 70.7 MB.

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Copyright information

© The Author(s) 2016

Open Access The articles published in this journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.National Digital Switching System Engineering & Technological Research CenterZhengzhouChina
  2. 2.Washington University in St. LouisSt. LouisUSA
  3. 3.AdobeSan FranciscoUSA

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