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An efficient unsupervised diffusion clustering algorithm with application to shape decomposition based on visibility context
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DOI：10.1016/j.image.2016.12.012.png)
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Abstract
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In this paper the two and three dimensional of a single shape partitioning problem is revisited by means of an iterative, non supervised, very fast and effective algorithm. The notion of visibility context is used as the shape signature, which actually provides a physical meaning to the representation. The visibility serves as a means to manipulate on the shape parts applying clustering techniques to the corresponding graph. Therefore, the decomposition problem is re-casted as a clustering problem. An unsupervised Diffusion Clustering Algorithm is proposed, which efficiently achieves to capture the functional shape parts. Although the proposed algorithm is developed and fits very well to the specific problem of the shape partitioning, its utility is undoubtedly much more general. Experimental results conducted on two and three dimensional shape databases are very promising.
Keywords:
Graph clustering
Shape decomposition
Visibility
Diffusion
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