Department of Computer Science, Dartmouth College, 6211 Sudikoff Lab, Hanover, NH 03755, USA.
IEEE Trans Pattern Anal Mach Intell. 2013 Feb;35(2):259-71. doi: 10.1109/TPAMI.2012.105.
In this paper, we present a new approach for establishing correspondences between sparse image features related by an unknown nonrigid mapping and corrupted by clutter and occlusion, such as points extracted from images of different instances of the same object category. We formulate this matching task as an energy minimization problem by defining an elaborate objective function of the appearance and the spatial arrangement of the features. Optimization of this energy is an instance of graph matching, which is in general an NP-hard problem. We describe a novel graph matching optimization technique, which we refer to as dual decomposition (DD), and demonstrate on a variety of examples that this method outperforms existing graph matching algorithms. In the majority of our examples, DD is able to find the global minimum within a minute. The ability to globally optimize the objective allows us to accurately learn the parameters of our matching model from training examples. We show on several matching tasks that our learned model yields results superior to those of state-of-the-art methods.
在本文中，我们提出了一种新的方法，用于建立通过未知非刚性映射相关的稀疏图像特征之间的对应关系，并对其进行了杂波和遮挡的干扰，例如从同一物体类别的不同实例的图像中提取的点。我们通过定义特征的外观和空间排列的精细目标函数，将此匹配任务表述为能量最小化问题。这种能量的优化是图匹配的一个实例，通常是一个 NP 难问题。我们描述了一种新颖的图匹配优化技术，我们称之为对偶分解（DD），并在各种示例中证明，该方法优于现有的图匹配算法。在我们的大多数示例中，DD 能够在一分钟内找到全局最小值。能够全局优化目标函数，使我们能够从训练示例中准确学习匹配模型的参数。我们在几个匹配任务中展示了，我们学习到的模型的结果优于最先进的方法。
