Name
Abstract | ||
|---|---|---|
Most previous works on salient object detection concentrate on 2D images. In this paper, we propose to explore the power of depth cue for predicting salient regions. Our basic assumption is that a salient object tends to stand out from its surroundings in 3D space. To measure the object-to-surrounding contrast, we propose a novel depth feature which works on a single depth map. Besides, we integrate the 3D spatial prior into our method for saliency refinement. By sparse sampling and representing the image using superpixels, our method works very fast, whose complexity is linear to the image resolution. To segment the salient object, we also develop a saliency based method using adaptive thresholding and GrabCut. The proposed method is evaluated on two large datasets designed for depth-aware salient object detection. The results compared with several state-of-the-art 2D and depth-aware methods show that our method has the most satisfactory overall performance. HighlightsWe proposed a new depth feature for salient region detection.Spatial prior is integrated for saliency refinement.A saliency-based object segmentation method is presented.We built the largest dataset for depth-aware salient object detection evaluation. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.image.2015.07.002 | Signal Processing: Image Communication |
Keywords | Field | DocType |
Salient object detection,Depth map,Center-surround difference | Computer vision,Pattern recognition,Segmentation,Salience (neuroscience),Computer science,GrabCut,Sampling (statistics),Artificial intelligence,Depth map,Thresholding,Image resolution,Salient | Journal |
Volume | Issue | ISSN |
38 | C | 0923-5965 |
Citations | PageRank | References |
31 | 0.87 | 41 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ran Ju | 1 | 108 | 7.87 |
| Liu Yan | 2 | 828 | 41.20 |
| Tongwei Ren | 3 | 328 | 30.22 |
| Ling Ge | 4 | 64 | 2.35 |
| Gangshan Wu | 5 | 275 | 36.63 |