IEEE Trans Image Process. 2014 Dec;23(12):5175-86. doi: 10.1109/TIP.2014.2362614. Epub 2014 Oct 9.
Feature point matching that incorporates pairwise constraints can be cast as an integer quadratic programming (IQP) problem. Since it is NP-hard, approximate methods are required. The optimal solution for IQP matching problem is discrete, binary, and thus sparse in nature. This motivates us to use sparse model for feature point matching problem. The main advantage of the proposed sparse feature point matching (SPM) method is that it generates sparse solution and thus naturally imposes the discrete mapping constraints approximately in the optimization process. Therefore, it can optimize the IQP matching problem in an approximate discrete domain. In addition, an efficient algorithm can be derived to solve SPM problem. Promising experimental results on both synthetic points sets matching and real-world image feature sets matching tasks show the effectiveness of the proposed feature point matching method.
特征点匹配，其中包含了两两约束条件，可以被描述为整数二次规划（IQP）问题。由于它是 NP 难问题，所以需要使用近似方法。IQP 匹配问题的最优解是离散的、二进制的，因此本质上是稀疏的。这促使我们使用稀疏模型来解决特征点匹配问题。所提出的稀疏特征点匹配（SPM）方法的主要优点是它生成稀疏解，因此可以在优化过程中自然地近似施加离散映射约束。因此，它可以在近似离散域中优化 IQP 匹配问题。此外，还可以推导出一种有效的算法来解决 SPM 问题。在合成点集匹配和真实世界图像特征集匹配任务上的实验结果表明了该特征点匹配方法的有效性。
