IEEE Trans Image Process. 2015 Dec;24(12):5995-6010. doi: 10.1109/TIP.2015.2496305. Epub 2015 Oct 30.
With the aim to improve the performance of feature matching, we present an unsupervised approach for adaptive description selection in the space of homographies. Inspired by the observation that the homographies of correct feature correspondences vary smoothly along the spatial domain, our approach stands on the unsupervised nature of feature matching, and can choose a good descriptor locally for matching each feature point, instead of using one global descriptor. To this end, the homography space serves as the domain for selecting various heterogeneous descriptors. Correspondences obtained by any descriptors are considered as points in the space, and their geometric coherence and spatial continuity are measured via computing the geodesic distances. In this way, mutual verification across different descriptors is allowed, and correct correspondences will be highlighted with a high degree of consistency short geodesic distances here. It follows that one-class SVM can be applied to identifying these correct correspondences, and achieves adaptive descriptor selection. The proposed approach is comprehensively compared with the state-of-the-art approaches, and evaluated on five benchmarks of image matching. The promising results manifest its effectiveness.
为了提高特征匹配的性能，我们提出了一种在单应性空间中自适应描述符选择的无监督方法。受正确特征对应点的单应变换在空间域中平滑变化这一观察结果的启发，我们的方法基于特征匹配的无监督性质，可以为匹配每个特征点选择一个合适的局部描述符，而不是使用一个全局描述符。为此，单应性空间可用作选择各种异构描述符的域。由任何描述符获得的对应点被视为空间中的点，并且通过计算测地距离来测量它们的几何一致性和空间连续性。这样，就可以允许不同描述符之间的相互验证，并且具有高度一致性的正确对应点将具有较短的测地距离。因此，可以应用单类 SVM 来识别这些正确的对应点，并实现自适应描述符选择。该方法与现有最先进的方法进行了全面比较，并在五个图像匹配基准上进行了评估。有前途的结果表明了它的有效性。
