Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.ins.2021.05.050 | Information Sciences |
Keywords | DocType | Volume |
Matchable image retrieval,Graph Convolutional Network,Structure-from-Motion | Journal | 573 |
ISSN | Citations | PageRank |
0020-0255 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shen Yan | 1 | 0 | 0.34 |
| Maojun Zhang | 2 | 314 | 48.74 |
| Shiming Lai | 3 | 0 | 0.68 |
| Yu Liu | 4 | 0 | 0.34 |
| Yang Peng | 5 | 2 | 1.72 |