Feature-aligned segmentation using correlation clustering

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....

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Bibliographic Details
Published in:Computational visual media (Beijing) 2017-06, Vol.3 (2), p.147-160
Main Authors: Zhuang, Yixin, Dou, Hang, Carr, Nathan, Ju, Tao
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Clustering
Computer Graphics
Computer Science
correlation clustering (CC)
Data mining
feature lines
Image Processing and Computer Vision
mesh segmentation
Research Article
Segmentation
User Interfaces and Human Computer Interaction
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Summary: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.
ISSN:2096-0433
2096-0662
DOI:10.1007/s41095-016-0071-3