Name
Abstract | ||
|---|---|---|
Saliency detection has been long researched. However, most existing algorithms can not uniformly highlight salient objects. To approach this problem, we propose a novel saliency detection algorithm based on the Local Single Gaussian Model (LSGM). First, we utilize a bottom-up model to generate an initial saliency map and construct a background dictionary and a foreground dictionary based on the initial saliency map, respectively. Then, a LSGM is used to obtain a LSGM-based map. Note that we construct a corresponding LSGM for each superpixel region and thus the LSGM is a dynamic model with geometric structure information. Finally, we integrate the LSGM-based saliency map and the initial bottom-up map with global information as the final saliency map. Extensive experiments on four public datasets show that our algorithm outperforms state-of-the-art methods. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | Bottom-up model, local single Gaussian model, saliency map |
Field | DocType | ISSN |
Computer vision,Saliency map,Pattern recognition,Salience (neuroscience),Computer science,Global information,Salient objects,Gaussian network model,Artificial intelligence | Conference | 1522-4880 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nan Xu | 1 | 5 | 0.73 |
| Yanqing Guo | 2 | 39 | 12.24 |
| Xiang-Wei Kong | 3 | 212 | 15.09 |