Simple Iterative Clustering on Graphs for Robust Model Fitting
Hailing Luo, Guobao Xiao, Hanzi Wang · 2018
In this paper, we propose a novel method, simple iterative clustering on graphs (SICG), to deal with robust model fitting problems. Specifically, we first construct a graph, where each vertex denotes a model hypothesis and each edge represents the similarity between two model hypotheses, for model fitting. We then propose a simple iterative clustering algorithm, which adapts the k-medoids clustering algorithm, to intuitively estimate model instances in data. The proposed SICG method is able to effectively fit and segment multiple-structure data contaminated with a large number of outliers and noises. Experimental results show that SICG achieves superior fitting results over several state-of-the-art model fitting methods on real images.