Robust Multi-object Matching via Iterative Reweighting of the Graph Connection Laplacian

TL;DR

This paper introduces a robust iterative method for multi-object matching that leverages the graph connection Laplacian to incorporate higher-order neighborhood information, outperforming existing approaches.

Contribution

A novel iterative reweighting strategy based on the graph connection Laplacian that improves robustness and accuracy in multi-object matching tasks.

Findings

Superior performance over state-of-the-art methods

Effective handling of higher-order neighborhood information

Validated on synthetic and real datasets

Abstract

We propose an efficient and robust iterative solution to the multi-object matching problem. We first clarify serious limitations of current methods as well as the inappropriateness of the standard iteratively reweighted least squares procedure. In view of these limitations, we suggest a novel and more reliable iterative reweighting strategy that incorporates information from higher-order neighborhoods by exploiting the graph connection Laplacian. We demonstrate the superior performance of our procedure over state-of-the-art methods using both synthetic and real datasets.