IEEE Trans Pattern Anal Mach Intell. 2015 Dec;37(12):2388-401. doi: 10.1109/TPAMI.2015.2420556.
We present an algorithm that integrates image co-segmentation into feature matching, and can robustly yield accurate and dense feature correspondences. Inspired by the fact that correct feature correspondences on the same object typically have coherent transformations, we cast the task of feature matching as a density estimation problem in the homography space. Specifically, we project the homographies of correspondence candidates into the parametric Hough space, in which geometric verification of correspondences can be activated by voting. The precision of matching is then boosted. On the other hand, we leverage image co-segmentation, which discovers object boundaries, to determine relevant voters and speed up Hough voting. In addition, correspondence enrichment can be achieved by inferring the concerted homographies that are propagated between the features within the same segments. The recall is hence increased. In our approach, feature matching and image co-segmentation are tightly coupled. Through an iterative optimization process, more and more correct correspondences are detected owing to object boundaries revealed by co-segmentation. The proposed approach is comprehensively evaluated. Promising experimental results on four datasets manifest its effectiveness.
我们提出了一种将图像共分割集成到特征匹配中的算法，该算法能够稳健地产生准确且密集的特征对应关系。受同一物体上的正确特征对应关系通常具有连贯变换这一事实的启发，我们将特征匹配任务作为同形空间中的密度估计问题来处理。具体来说，我们将对应候选者的单应性投影到参数霍夫空间中，其中可以通过投票来激活对应关系的几何验证。然后提高匹配的精度。另一方面，我们利用图像共分割来发现对象边界，以确定相关的投票者并加快霍夫投票。此外，可以通过推断在同一段内的特征之间传播的协调单应性来实现对应关系的丰富。从而提高召回率。在我们的方法中，特征匹配和图像共分割是紧密结合的。通过迭代优化过程，由于共分割揭示的对象边界，检测到越来越多的正确对应关系。该方法得到了全面评估。四个数据集上的有前途的实验结果证明了其有效性。
