  Recently, some hypergraph-based methods have been proposed to deal with the
problem of model fitting in computer vision, mainly due to the superior
capability of hypergraph to represent the complex relationship between data
points. However, a hypergraph becomes extremely complicated when the input data
include a large number of data points (usually contaminated with noises and
outliers), which will significantly increase the computational burden. In order
to overcome the above problem, we propose a novel hypergraph optimization based
model fitting (HOMF) method to construct a simple but effective hypergraph.
Specifically, HOMF includes two main parts: an adaptive inlier estimation
algorithm for vertex optimization and an iterative hyperedge optimization
algorithm for hyperedge optimization. The proposed method is highly efficient,
and it can obtain accurate model fitting results within a few iterations.
Moreover, HOMF can then directly apply spectral clustering, to achieve good
fitting performance. Extensive experimental results show that HOMF outperforms
several state-of-the-art model fitting methods on both synthetic data and real
images, especially in sampling efficiency and in handling data with severe
outliers.
