Nonnegative Matrix Underapproximation for Robust Multiple Model Fitting
This work addresses robust model fitting for computer vision applications, but it is incremental as it builds on nonnegative matrix factorization with a new constraint.
The paper tackles the problem of robust multiple model fitting by introducing a nonnegative matrix underapproximation (NMU) algorithm, which achieves state-of-the-art results in estimating multiple fundamental matrices and homographies, outperforming existing alternatives.
In this work, we introduce a highly efficient algorithm to address the nonnegative matrix underapproximation (NMU) problem, i.e., nonnegative matrix factorization (NMF) with an additional underapproximation constraint. NMU results are interesting as, compared to traditional NMF, they present additional sparsity and part-based behavior, explaining unique data features. To show these features in practice, we first present an application to the analysis of climate data. We then present an NMU-based algorithm to robustly fit multiple parametric models to a dataset. The proposed approach delivers state-of-the-art results for the estimation of multiple fundamental matrices and homographies, outperforming other alternatives in the literature and exemplifying the use of efficient NMU computations.