Name
Abstract | ||
|---|---|---|
As an important problem in computer vision, saliency detection is essential for image segmentation, super-resolution, object recognition, and so on. We propose a saliency detection method for images. Instead of using contrast between salient regions and their surrounding areas, both cues from salient and nonsalient regions are considered in our study. Based on these cues, an improved multimanifold ranking algorithm is proposed. In our algorithm, features from multiple views are utilized and the different contributions of these multiview features are taken into account. Moreover, an iterative updating optimization scheme is explored to solve the objective function, during which the feature fusion is performed. After two-stage ranking by the improved multimanifold ranking algorithm, each image patch can be assigned a ranking score, which determines the final saliency. The proposed method is evaluated on four public datasets and is compared with the state-of-the-art methods. Experimental results indicate that the proposed method outperforms existing schemes both in qualitative and quantitative comparisons. (C) 2014 SPIE and IS&T |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1117/1.JEI.23.6.061113 | JOURNAL OF ELECTRONIC IMAGING |
Keywords | Field | DocType |
saliency detection,multiple views,multimanifold ranking,boundary prior | Computer vision,Feature fusion,Ranking,Pattern recognition,Kadir–Brady saliency detector,Salience (neuroscience),Computer science,Image segmentation,Artificial intelligence,Superresolution,Salient,Cognitive neuroscience of visual object recognition | Journal |
Volume | Issue | ISSN |
23 | 6 | 1017-9909 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanjiao Shi | 1 | 34 | 3.14 |
| Yugen Yi | 2 | 92 | 15.25 |
| Ke Zhang | 3 | 13 | 1.91 |
| Jun Kong | 4 | 27 | 4.86 |
| Ming Zhang | 5 | 16 | 1.93 |
| Jianzhong Wang | 6 | 214 | 17.72 |