论文标题
本地功能描述符的在线不变性选择
Online Invariance Selection for Local Feature Descriptors
论文作者
论文摘要
是不变的，或者不变的：这是有关本地描述符的这项工作中提出的问题。当前功能描述符的局限性是概括和判别能力之间的权衡：更多的不变性意味着较少的信息描述符。我们建议通过在本地描述符中的不变性以及在线选择最合适的不变性来克服这一限制。我们的框架包括对具有不同级别不变性的多个局部描述符和编码图像区域变化的元描述符的联合学习。这些元描述符跨图像的相似性用于在与本地描述符匹配时选择正确的不变性。我们的方法以描述符（LISRD）在运行时命名的本地不变性选择，使描述符能够适应图像的不良变化，同时在不需要不变时保持歧视。我们证明，当在具有昼夜照明的挑战数据集评估时，我们的方法可以在几个匹配任务中提高当前描述符和优于最先进的描述符的性能。
To be invariant, or not to be invariant: that is the question formulated in this work about local descriptors. A limitation of current feature descriptors is the trade-off between generalization and discriminative power: more invariance means less informative descriptors. We propose to overcome this limitation with a disentanglement of invariance in local descriptors and with an online selection of the most appropriate invariance given the context. Our framework consists in a joint learning of multiple local descriptors with different levels of invariance and of meta descriptors encoding the regional variations of an image. The similarity of these meta descriptors across images is used to select the right invariance when matching the local descriptors. Our approach, named Local Invariance Selection at Runtime for Descriptors (LISRD), enables descriptors to adapt to adverse changes in images, while remaining discriminative when invariance is not required. We demonstrate that our method can boost the performance of current descriptors and outperforms state-of-the-art descriptors in several matching tasks, when evaluated on challenging datasets with day-night illumination as well as viewpoint changes.
