Name
Abstract | ||
|---|---|---|
Detecting salient objects in challenging images attracts increasing attention as many applications require more robust method to deal with complex images from the Internet. Prior methods produce poor saliency maps in challenging cases mainly due to the complex patterns in the background and internal color edges in the foreground. The former problem may introduce noises into saliency maps and the later forms the difficulty in determining object boundaries. Observing that depth map can supply layering information and more reliable boundary, we improve salient object detection by integrating two features: color information and depth information which are calculated from stereo images. The two features collaborate in a two-stage framework. In the object location stage, depth mainly helps to produce a noise-filtered salient patch, which indicates the location of the object. In the object boundary inference stage, boundary information is encoded in a graph using both depth and color information, and then we employ the random walk to infer more reliable boundaries and obtain the final saliency map. We also build a data set containing 100+ stereo pairs to test the effectiveness of our method. Experiments show that our depth-plus-color based method significantly improves salient object detection compared with previous color-based methods. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s00371-014-1059-6 | The Visual Computer |
Keywords | Field | DocType |
Salient object detection, Depth information, Color information, Stereo images | Computer vision,Graph,Saliency map,Salient object detection,Pattern recognition,Inference,Salience (neuroscience),Random walk,Computer science,Artificial intelligence,Depth map,Salient | Journal |
Volume | Issue | ISSN |
32 | 1 | 1432-2315 |
Citations | PageRank | References |
12 | 0.53 | 45 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan-Long Tang | 1 | 20 | 1.67 |
| Ruofeng Tong | 2 | 466 | 49.69 |
| Min Tang | 3 | 623 | 51.33 |
| Yun Zhang | 4 | 137 | 5.56 |