Hypergraph optimization for multi-structural geometric model fitting

Abstract

Recently, some hypergraph-based methods have been pro- posed 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. How- ever, a hypergraph becomes extremely complicated when the input data include a large number of data points (usually con- taminated 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 ver- tex optimization and an iterative hyperedge optimization al- gorithm for hyperedge optimization. The proposed method is highly efficient, and it can obtain accurate model fitting re- sults within a few iterations. Moreover, HOMF can then di- rectly apply spectral clustering, to achieve good fitting per- formance. 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