Name
Abstract | ||
|---|---|---|
In content-based image retrieval (CBIR), one of the most challenging and ambiguous tasks is to correctly understand the human query intention and measure its semantic relevance with images in the database. Due to the impressive capability of visual saliency in predicting human visual attention that is closely related to the query intention, this paper attempts to explicitly discover the essential ... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TIP.2019.2913513 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Feature extraction,Image retrieval,Visualization,Semantics,Streaming media,Task analysis,Reliability | Computer vision,Pattern recognition,Convolutional neural network,Visualization,Salience (neuroscience),Image retrieval,Feature extraction,Artificial intelligence,Discriminative model,Semantics,Mathematics,Content-based image retrieval | Journal |
Volume | Issue | ISSN |
28 | 9 | 1057-7149 |
Citations | PageRank | References |
0 | 0.34 | 30 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shikui Wei | 1 | 274 | 25.75 |
| lixin liao | 2 | 3 | 2.11 |
| Jia Li | 3 | 524 | 42.09 |
| Qinjie Zheng | 4 | 3 | 0.71 |
| Fei Yang | 5 | 21 | 14.49 |
| Yao Zhao | 6 | 1926 | 219.11 |