Please use this identifier to cite or link to this item: https://doi.org/10.1109/CVPR.2012.6247723
Title: Efficient structure detection via random consensus graph
Authors: Liu, H. 
Yan, S. 
Issue Date: 2012
Source: Liu, H.,Yan, S. (2012). Efficient structure detection via random consensus graph. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition : 574-581. ScholarBank@NUS Repository. https://doi.org/10.1109/CVPR.2012.6247723
Abstract: In this paper, we propose an efficient method to detect the underlying structures in data. The same as RANSAC, we randomly sample MSSs (minimal size samples) and generate hypotheses. Instead of analyzing each hypothesis separately, the consensus information in all hypotheses is naturally fused into a hypergraph, called random consensus graph, with real structures corresponding to its dense subgraphs. The sampling process is essentially a progressive refinement procedure of the random consensus graph. Due to the huge number of hyperedges, it is generally inefficient to detect dense subgraphs on random consensus graphs. To overcome this issue, we construct a pairwise graph which approximately retains the dense subgraphs of the random consensus graph. The underlying structures are then revealed by detecting the dense subgraphs of the pair-wise graph. Since our method fuses information from all hypotheses, it can robustly detect structures even under a small number of MSSs. The graph framework enables our method to simultaneously discover multiple structures. Besides, our method is very efficient, and scales well for large scale problems. Extensive experiments illustrate the superiority of our proposed method over previous approaches, achieving several orders of magnitude speedup along with satisfactory accuracy and robustness. © 2012 IEEE.
Source Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
URI: http://scholarbank.nus.edu.sg/handle/10635/43277
ISBN: 9781467312264
ISSN: 10636919
DOI: 10.1109/CVPR.2012.6247723
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