Active co-analysis of a set of shapes

Yunhai Wang*, Shmulik Asafi, Oliver Van Kaick, Hao Zhang, Daniel Cohen-Or, Baoquan Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Unsupervised co-analysis of a set of shapes is a difficult problem since the geometry of the shapes alone cannot always fully describe the semantics of the shape parts. In this paper, we propose a semi-supervised learning method where the user actively assists in the co-analysis by iteratively providing inputs that progressively constrain the system. We introduce a novel constrained clustering method based on a spring system which embeds elements to better respect their inter-distances in feature space together with the usergiven set of constraints. We also present an active learning method that suggests to the user where his input is likely to be the most effective in refining the results. We show that each single pair of constraints affects many relations across the set. Thus, the method requires only a sparse set of constraints to quickly converge toward a consistent and error-free semantic labeling of the set.

Original languageEnglish
Article number165
JournalACM Transactions on Graphics
Issue number6
StatePublished - Nov 2012


  • Active learning
  • Semi-supervised learning


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