Robust Multiple Model Fitting with Preference Analysis and Low-rank Approximation

Luca Magri and Andrea Fusiello

Abstract

This paper deals with the extraction of multiple models from outlier-contaminated data. The method we present is based on preference analysis and low rank approximation. After representing points in a conceptual space, Robust PCA (Principal Component Analysis) and Symmetric NMF (Non negative Matrix Factorization) are employed to reduce the multi-model fitting problem to many single-fitting problems, which in turn are solved with a strategy that resembles MSAC (M-estimator SAmple Consensus). Experimental validation on public, real data-sets demonstrates that our method compares favourably with the state of the art.

Session

Poster 1

Files

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DOI

10.5244/C.29.20
https://dx.doi.org/10.5244/C.29.20

Citation

Luca Magri and Andrea Fusiello. Robust Multiple Model Fitting with Preference Analysis and Low-rank Approximation. In Xianghua Xie, Mark W. Jones, and Gary K. L. Tam, editors, Proceedings of the British Machine Vision Conference (BMVC), pages 20.1-20.12. BMVA Press, September 2015.

Bibtex

@inproceedings{BMVC2015_20,
	title={Robust Multiple Model Fitting with Preference Analysis and Low-rank Approximation},
	author={Luca Magri and Andrea Fusiello},
	year={2015},
	month={September},
	pages={20.1-20.12},
	articleno={20},
	numpages={12},
	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
	publisher={BMVA Press},
	editor={Xianghua Xie, Mark W. Jones, and Gary K. L. Tam},
	doi={10.5244/C.29.20},
	isbn={1-901725-53-7},
	url={https://dx.doi.org/10.5244/C.29.20}
}