Name
Abstract | ||
|---|---|---|
This study addresses the issue of region representation in region-based image retrieval (RBIR). In order to reduce the user's burden of selecting the region of interest, a statistical index called visual region importance (RI) is constructed to describe the region. By learning from user's current and historical feedback information, visual RI can be automatically updated and semantic RI can be obtained. Furthermore, adaptive learning RI and memory learning RI (MLRI) techniques for RBIR system have been presented. Specifically, the MLRI can mitigate the negative influence of interference regions well. Extensive experiments on the Corel-1000 dataset and the Caltech-256 dataset demonstrate that the proposed frameworks are effective, are robust and achieve significantly better performance than the other existing methods. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1049/iet-cvi.2014.0119 | IET Computer Vision |
Keywords | Field | DocType |
content-based retrieval,feature extraction,image retrieval,learning (artificial intelligence),caltech-256 dataset,corel-1000 dataset,mlri techniques,rbir system,adaptive learning ri techniques,adaptive learning region importance,feedback information,interference regions,memory learning ri techniques,region representation issue,region-based image retrieval,semantic ri,statistical index,visual region importance | Computer vision,Information retrieval,Image retrieval,Interference (wave propagation),Artificial intelligence,Region of interest,Memory learning,Adaptive learning,Mathematics | Journal |
Volume | Issue | ISSN |
9 | 3 | 1751-9632 |
Citations | PageRank | References |
1 | 0.35 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiao-Hui Yang | 1 | 10 | 3.38 |
| Feiya Lv | 2 | 1 | 0.69 |
| Li-jun Cai | 3 | 37 | 13.57 |
| Dengfeng Li | 4 | 39 | 2.69 |