Hierarchical Representation via Message Propagation for Robust Model Fitting

TL;DR

This paper introduces HRMP, a hierarchical message propagation method that improves robust model fitting by effectively handling outliers and estimating multiple model parameters simultaneously.

Contribution

It proposes a novel hierarchical representation and algorithms that enhance robustness and accuracy in multi-model fitting with outlier contamination.

Findings

Outperforms state-of-the-art methods in accuracy

Handles large outlier ratios effectively

Achieves faster fitting in experiments

Abstract

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.