论文标题
通过迭代重新加权laplacian的迭代重新加权，可靠的多对象匹配
Robust Multi-object Matching via Iterative Reweighting of the Graph Connection Laplacian
论文作者
论文摘要
我们为多目标匹配问题提出了有效且可靠的迭代解决方案。我们首先阐明了当前方法的严重局限性以及标准迭代重量最小二乘程序的不适当性。鉴于这些局限性，我们建议一种新颖，更可靠的迭代重新加权策略，该策略通过利用图形连接laplacian来结合高阶邻里的信息。我们使用合成数据集证明了我们的过程的优越性能，而不是最先进的方法。
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.
