Lin, S.Xiao, G.Yan, Y.Suter, D.Wang, H.2021-09-202021-09-202019Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 2019, vol.33, iss.01, pp.8730-873797815773580912159-53992374-3468https://hdl.handle.net/2440/132106Recently, 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 outliersen© 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.Conference paper10.1609/aaai.v33i01.330187302021-09-20548324Suter, D. [0000-0001-6306-3023]