Name
Abstract | ||
|---|---|---|
When dealing with objects with complex structures, saliency detection confronts a critical problem - namely that detection accuracy could be adversely affected if salient foreground or background in an image contains small-scale high-contrast patterns. This issue is common in natural images and forms a fundamental challenge for prior methods. We tackle it from a scale point of view and propose a multi-layer approach to analyze saliency cues. The final saliency map is produced in a hierarchical model. Different from varying patch sizes or downsizing images, our scale-based region handling is by finding saliency values optimally in a tree model. Our approach improves saliency detection on many images that cannot be handled well traditionally. A new dataset is also constructed. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CVPR.2013.153 | CVPR |
Keywords | DocType | Volume |
complex structure,critical problem,saliency cue,detection accuracy,saliency values optimally,multi-layer approach,tree model,hierarchical saliency detection,hierarchical model,final saliency map,saliency detection,psychology,graphical models,benchmark testing | Conference | 2013 |
Issue | ISSN | Citations |
1 | 1063-6919 | 160 |
PageRank | References | Authors |
3.69 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiong Yan | 1 | 630 | 22.47 |
| Li Xu | 2 | 1713 | 54.04 |
| Jianping Shi | 3 | 920 | 43.57 |
| Jiaya Jia | 4 | 5082 | 217.90 |