Name
Abstract | ||
|---|---|---|
Salient object detection is an important process for machines to understand visual contents as humans. Typically, most previous studies on salient object detection infer salient map by only using the visual features. In this paper, we propose a new paradigm on salient object detection, which aims at producing more reliable results by mining the context information from user's annotated tags. To address this problem, we firstly construct a large scale salient object dataset, which includes 5429 images from the NUSWIDE dataset a real world web image database from National University of Singapore with tag information and accurate human-labeled masks. Moreover, a specialised conditional random field CRF model is also proposed which takes account of both tag contexts and appearance cues. Extensive experiments show the effectiveness and superiority of our model which can accurately locate the salient objects. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1504/IJSNET.2017.083535 | IJSNet |
Keywords | Field | DocType |
saliency, salient object detection, social images, tags | Conditional random field,Salient object detection,Pattern recognition,Computer science,Salience (neuroscience),Salient objects,Artificial intelligence,Web image,Salient | Journal |
Volume | Issue | ISSN |
23 | 4 | 1748-1279 |
Citations | PageRank | References |
0 | 0.34 | 36 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ye Liang | 1 | 6 | 5.39 |
| Congyan Lang | 2 | 353 | 39.20 |
| Jian Yu | 3 | 1347 | 149.17 |
| Hongzhe Liu | 4 | 56 | 10.93 |