Hierarchical Representation via Message Propagation for Robust Model Fitting
This work addresses the problem of robustly fitting multiple model instances from data corrupted by outliers, which is important for computer vision and machine learning practitioners dealing with noisy real-world data.
This paper introduces a Hierarchical Representation via Message Propagation (HRMP) method for robust model fitting, which combines consensus and preference analysis to estimate parameters of multiple model instances from outlier-corrupted data. The method accurately estimates the number and parameters of multiple model instances and handles multi-structural data with many outliers, outperforming state-of-the-art methods in fitting accuracy and speed on synthetic and real image data.
In this paper, we propose a novel hierarchical representation via message propagation (HRMP) method for robust model fitting, which simultaneously takes advantages of both the consensus analysis and the preference analysis to estimate the parameters of multiple model instances from data corrupted by outliers, for robust model fitting. Instead of analyzing the information of each data point or each model hypothesis independently, we formulate the consensus information and the preference information as a hierarchical representation to alleviate the sensitivity to gross outliers. Specifically, we firstly construct a hierarchical representation, which consists of a model hypothesis layer and a data point layer. The model hypothesis layer is used to remove insignificant model hypotheses and the data point layer is used to remove gross outliers. Then, based on the hierarchical representation, we propose an effective hierarchical message propagation (HMP) algorithm and an improved affinity propagation (IAP) algorithm to prune insignificant vertices and cluster the remaining data points, respectively. The proposed HRMP can not only accurately estimate the number and parameters of multiple model instances, but also handle multi-structural data contaminated with a large number of outliers. Experimental results on both synthetic data and real images show that the proposed HRMP significantly outperforms several state-of-the-art model fitting methods in terms of fitting accuracy and speed.