Hypergraph Optimization for Multi-structural Geometric Model Fitting
This addresses the problem of efficient and robust geometric model fitting for computer vision applications, representing an incremental improvement over existing hypergraph methods.
The paper tackles the computational burden of hypergraph-based model fitting in computer vision by proposing HOMF, a method that constructs a simpler hypergraph through vertex and hyperedge optimization, achieving accurate results within few iterations and outperforming state-of-the-art methods in sampling efficiency and outlier handling.
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.