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Unsupervised Co-Segmentation of a Set of Shapes via Descriptor-Space Spectral Clustering
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@MISC{Sidi_unsupervisedco-segmentation,
author = {Oana Sidi and Oliver Kaick and Yanir Kleiman and Hao Zhang and Daniel Cohen-or},
title = {Unsupervised Co-Segmentation of a Set of Shapes via Descriptor-Space Spectral Clustering},
year = {}
}
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Abstract
We introduce an algorithm for unsupervised co-segmentation of a set of shapes so as to reveal the semantic shape parts and establish their correspondence across the set. The input set may exhibit significant shape variability where the shapes do not admit proper spatial alignment and the corresponding parts in any pair of shapes may be geometrically dissimilar. Our algorithm can handle such challenging input sets since, first, we perform co-analysis in a descriptor space, where a combination of shape descriptors relates the parts independently of their pose, location, and cardinality. Secondly, we exploit a key enabling feature of the input set, namely, dissimilar parts may be “linked ” through third-parties present in the set. The links are derived from the pairwise similarities between the parts ’ descriptors. To reveal such linkages, which may manifest themselves as anisotropic and non-linear structures in the descriptor space, we perform spectral clustering with the aid of diffusion maps. We show that with our approach, we are able to co-segment sets of shapes that possess significant variability, achieving results that are close to those of a supervised approach. Keywords: Co-segmentation, shape correspondence, spectral clustering, diffusion maps. Links: DL PDF 1
Keyphrases
descriptor-space spectral clustering diffusion map descriptor space spatial alignment challenging input set input set key enabling feature shape descriptor non-linear structure co-segment set unsupervised co-segmentation dissimilar part significant variability semantic shape part corresponding part dl pdf significant shape variability spectral clustering supervised approach part descriptor shape correspondence pairwise similarity
