Name
Abstract | ||
|---|---|---|
In this paper, we present a saliency guided image retargeting method. Our bio-inspired saliency measure integrates three factors: dissimilarity, spatial distance and central bias, and these three factors are supported by research on human vision system (HVS). To produce perceptual satisfactory retargeting images, we use the saliency map as the importance map in the retargeting method. We suppose that saliency maps can indicate informative regions, and filter out background in images. Experimental results demonstrate that our method outperforms previous retargeting method guided by the gray image on distorting dominant objects less. And further comparison between various saliency detection methods show that retargeting method using our saliency measure maintains more parts of foreground. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-24955-6_22 | ICONIP (1) |
Keywords | Field | DocType |
previous retargeting method,retargeting method,bio-inspired visual saliency detection,gray image,perceptual satisfactory retargeting image,central bias,saliency measure,bio-inspired saliency measure,image retargeting,importance map,various saliency detection method,saliency map | Computer vision,Saliency map,Machine vision,Pattern recognition,Computer science,Salience (neuroscience),Seam carving,Retargeting,Artificial intelligence,Perception,Visual saliency | Conference |
Volume | ISSN | Citations |
7062 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 19 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lijuan Duan | 1 | 215 | 26.13 |
| Chunpeng Wu | 2 | 469 | 19.00 |
| Haitao Qiao | 3 | 0 | 0.34 |
| Jili Gu | 4 | 1 | 1.04 |
| Jun Miao | 5 | 220 | 22.17 |
| Laiyun Qing | 6 | 337 | 24.66 |
| Zhen Yang | 7 | 23 | 3.88 |