Robust multi-object matching via iterative reweighting of the graph connection Laplacian

Yunpeng Shi, Shaohan Li, Gilad Lerman

Research output: Contribution to journalConference articlepeer-review

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 provide partial theoretical guarantees and demonstrate the superior performance of our procedure over state-of-the-art methods using both synthetic and real datasets.

Original languageEnglish (US)
JournalAdvances in Neural Information Processing Systems
Volume2020-December
StatePublished - 2020
Event34th Conference on Neural Information Processing Systems, NeurIPS 2020 - Virtual, Online
Duration: Dec 6 2020Dec 12 2020

Bibliographical note

Funding Information:
This work was supported by NSF award DMS-18-21266.

Publisher Copyright:
© 2020 Neural information processing systems foundation. All rights reserved.

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