Name
Abstract | ||
|---|---|---|
Image retrieval has achieved remarkable improvements with the rapid progress on visual representation and indexing techniques. Given a query image, search engines are expected to retrieve relevant results in which the top-ranked short list is of most value to users. However, it is challenging to measure the retrieval quality on-the-fly without direct user feedbacks. In this paper, we aim at evalua... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TIP.2018.2864919 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Image retrieval,Feature extraction,Correlation,Search engines,Quality assessment,Visualization | Search engine,Information retrieval,Pattern recognition,Convolutional neural network,Visualization,Image retrieval,Search engine indexing,Feature extraction,Ground truth,Artificial intelligence,Mathematics,Discounted cumulative gain | Journal |
Volume | Issue | ISSN |
27 | 12 | 1057-7149 |
Citations | PageRank | References |
3 | 0.38 | 15 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shaoyan Sun | 1 | 45 | 3.90 |
| Wengang Zhou | 2 | 1226 | 79.31 |
| Qi Tian | 3 | 6443 | 331.75 |
| Ming Yang | 4 | 3471 | 162.50 |
| Houqiang Li | 5 | 2090 | 172.30 |