Name
Abstract | ||
|---|---|---|
Deep learning has been applied to saliency detection in recent years. The superior performance has proved that deep networks can model the semantic properties of salient objects. Yet it is difficult for a deep network to discriminate pixels belonging to similar receptive fields around the object boundaries, thus deep networks may output maps with blurred saliency and inaccurate boundaries. To tackle such an issue, in this work, we propose a deep Level Set network to produce compact and uniform saliency maps. Our method drives the network to learn a Level Set function for salient objects so it can output more accurate boundaries and compact saliency. Besides, to propagate saliency information among pixels and recover full resolution saliency map, we extend a superpixel-based guided filter to be a layer in the network. The proposed network has a simple structure and is trained end-to-end. During testing, the network can produce saliency maps by efficiently feedforwarding testing images at a speed over 12FPS on GPUs. Evaluations on benchmark datasets show that the proposed method achieves state-of-the-art performance. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CVPR.2017.65 | 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) |
Keywords | Field | DocType |
salient object detection,deep learning,object boundaries,blurred saliency,deep Level Set network,saliency information,resolution saliency map,saliency maps,GPU,superpixel-based guided filter | Object detection,Computer vision,Pattern recognition,Kadir–Brady saliency detector,Salience (neuroscience),Computer science,Level set,Feature extraction,Image segmentation,Pixel,Artificial intelligence,Deep learning | Conference |
Volume | Issue | ISSN |
2017 | 1 | 1063-6919 |
ISBN | Citations | PageRank |
978-1-5386-0458-8 | 26 | 0.65 |
References | Authors | |
43 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ping Hu | 1 | 43 | 5.12 |
| Bing Shuai | 2 | 237 | 12.51 |
| Jun Liu | 3 | 671 | 30.44 |
| Gang Wang | 4 | 2869 | 135.49 |