Name
Abstract | ||
|---|---|---|
AbstractThis article studies the correspondence problem for semantically similar images, which is challenging due to the joint visual and geometric deformations. We introduce the Flip-aware Distance Ratio method (FDR) to solve this problem from the perspective of geometric structure analysis. First, a distance ratio constraint is introduced to enforce the geometric consistencies between images with large visual variations, whereas local geometric jitters are tolerated via a smoothness term. For challenging cases with symmetric structures, our proposed method exploits Curl to suppress the mismatches. Subsequently, image correspondence is formulated as a permutation problem, for which we propose a Gradient Guided Simulated Annealing (GGSA) algorithm to perform a robust discrete optimization. Experiments on simulated and real-world datasets, where both visual and geometric deformations are present, indicate that our method significantly improves the baselines for both visually and semantically similar images. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3441576 | ACM Transactions on Multimedia Computing, Communications, and Applications |
Keywords | DocType | Volume |
Curl, image correspondence, bilateral symmetry | Journal | 17 |
Issue | ISSN | Citations |
3 | 1551-6857 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Wang | 1 | 35 | 8.87 |
| Liang Dong | 2 | 326 | 52.32 |
| Xiaochun Cao | 3 | 1986 | 131.55 |
| Yuanfang Guo | 4 | 95 | 18.21 |