Name
Abstract | ||
|---|---|---|
Various kinds of features prove to be effective for content-based image retrieval. However, due to the diversity of image contents, a descriptor may achieve impressive performance on specific images while becoming invalid on others. Although some efforts have been made to combine features as complementary counterparts, proper weighting scheme is still a challenge for fast and accurate retrieval. In this paper, we propose an effective fusion method, termed as Topo-correlation (Topo), where the importance of each feature is measured by cross-view correlations on local affinity graphs. Specifically, the weights of similarities are node-sensitive as well as modality-sensitive, thus boosting the results of good cues while depressing adverse factors for individual images. By estimating the consensus of similarity scores with regard to a query-driven criterion, the weighted graphs are generated efficiently with low computational complexity. Extensive experimental results on four benchmarks demonstrate the superiority of the proposed approach over the state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TCSVT.2019.2944009 | IEEE Transactions on Circuits and Systems for Video Technology |
Keywords | DocType | Volume |
Image retrieval,Correlation,Manifolds,Image edge detection,Computational complexity,Indexes | Journal | 30 |
Issue | ISSN | Citations |
10 | 1051-8215 | 1 |
PageRank | References | Authors |
0.35 | 17 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying Li | 1 | 11 | 1.82 |
| Xiang-Wei Kong | 2 | 212 | 15.09 |
| Haiyan Fu | 3 | 10 | 2.85 |
| Qi Tian | 4 | 1 | 0.35 |