Conventional image retrieval techniques for Structure-from-Motion (SfM) are limited in their ability to effectively distinguish symmetric or repetitive textured patterns and cannot guarantee an accurate generation of pairwise matches without costly redundancy. In this paper, we formulate the image retrieval task as a node binary classification problem with graph data: if a candidate node is marked as positive, it is believed to share the same scene with the query image. The key idea of our approach is that the local context in the feature space around a query image contains abundant information about the matchable relation between the image and its neighbours. By constructing a subgraph surrounding the query image as input data, we adopt a learnable Graph Convolutional Network (GCN) to determine whether nodes in the subgraph have overlapping regions with the query photograph. Experiments demonstrate that our method performs remarkably well on a challenging dataset of highly ambiguous and duplicated scenes. Furthermore, compared with state-of-the-art matchable retrieval methods, the proposed approach significantly reduces unnecessary attempted matches without sacrificing the accuracy and completeness of reconstruction.

用于运动结构（SfM）的传统图像检索技术在有效区分对称或重复纹理图案的能力方面受到限制，并且无法保证在没有代价高昂的冗余的情况下准确生成成对匹配。在本文中，我们将图像检索任务表述为一个使用图数据的节点二分类问题：如果候选节点被标记为正，则认为它与查询图像共享相同的场景。我们方法的关键思想是查询图像周围特征空间中的局部上下文包含有关图像与其邻居之间可匹配关系的丰富信息。通过构建一个围绕查询图像的子图作为输入数据，我们采用了一个可学习的图卷积网络 (GCN) 确定子图中的节点是否与查询照片有重叠区域。实验表明，我们的方法在高度模糊和重复场景的具有挑战性的数据集上表现得非常好。此外，与最先进的可匹配检索方法相比，所提出的方法显着减少了不必要的尝试匹配，而不会牺牲重建的准确性和完整性。