Robust Multi-object Matching via Iterative Reweighting of the Graph Connection Laplacian
This work addresses robust matching in computer vision or related fields, offering a novel method for a known bottleneck.
The paper tackles the multi-object matching problem by proposing an iterative reweighting strategy that uses the graph connection Laplacian to incorporate higher-order neighborhood information, demonstrating superior performance over state-of-the-art methods on synthetic and real datasets.
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.