Mode-Seeking on Hypergraphs for Robust Geometric Model Fitting
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Date
2015
Authors
Wang, H.
Xiao, G.
Yan, Y.
Suter, D.
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Conference paper
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Proceedings / IEEE International Conference on Computer Vision. IEEE International Conference on Computer Vision, 2015, vol.Proceedings 2015 IEEE International Conference on Computer Vision, pp.2902-2910
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Hanzi Wang, Guobao Xiao, Yan Yan, David Suter
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IEEE International Conference on Computer Vision (ICCV) (11 Dec 2015 - 18 Dec 2015 : Santiago, Chile)
Abstract
In this paper, we propose a novel geometric model fitting method, called Mode-Seeking on Hypergraphs (MSH), to deal with multi-structure data even in the presence of severe outliers. The proposed method formulates geometric model fitting as a mode seeking problem on a hypergraph in which vertices represent model hypotheses and hyperedges denote data points. MSH intuitively detects model instances by a simple and effective mode seeking algorithm. In addition to the mode seeking algorithm, MSH includes a similarity measure between vertices on the hypergraph and a “weight-aware sampling” technique. The proposed method not only alleviates sensitivity to the data distribution, but also is scalable to large scale problems. Experimental results further demonstrate that the proposed method has significant superiority over the state-of-the-art fitting methods on both synthetic data and real images.
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© 2015 IEEE