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Type: Journal article
Title: Searching for representative modes on hypergraphs for robust geometric model fitting
Author: Wang, H.
Xiao, G.
Yan, Y.
Suter, D.
Citation: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019; 41(3):697-711
Publisher: IEEE
Issue Date: 2019
ISSN: 0162-8828
Statement of
Hanzi Wang, Guobao Xiao, Yan Yan and David Suter
Abstract: In this paper, we propose a simple and effective geometric model fitting method to fit and segment multi-structure data even in the presence of severe outliers. We cast the task of geometric model fitting as a representative mode-seeking problem on hypergraphs. Specifically, a hypergraph is first constructed, where the vertices represent model hypotheses and the hyperedges denote data points. The hypergraph involves higher-order similarities (instead of pairwise similarities used on a simple graph), and it can characterize complex relationships between model hypotheses and data points. In addition, we develop a hypergraph reduction technique to remove "insignificant" vertices while retaining as many "significant" vertices as possible in the hypergraph. Based on the simplified hypergraph, we then propose a novel mode-seeking algorithm to search for representative modes within reasonable time. Finally, the proposed mode-seeking algorithm detects modes according to two key elements, i.e., the weighting scores of vertices and the similarity analysis between vertices. Overall, the proposed fitting method is able to efficiently and effectively estimate the number and the parameters of model instances in the data simultaneously. Experimental results demonstrate that the proposed method achieves significant superiority over several state-of-the-art model fitting methods on both synthetic data and real images.
Rights: © 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See for more information.
RMID: 0030093416
DOI: 10.1109/TPAMI.2018.2803173
Grant ID:
Appears in Collections:Computer Science publications

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