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 favorably with the state of the art.

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

Luca Magri;
2015

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 favorably with the state of the art.
Procedings of the British Machine Vision Conference 2015
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1188357
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