Name
Abstract | ||
|---|---|---|
Salient object detection is a challenging problem in many areas such as image segmentation and object recognition. Many approaches reveal that the background of an image usually lies in a low-dimensional subspace, while the salient regions perform as noises. Conventional methods apply nuclear norm minimization to recover the low-rank background to get the saliency. However, the nuclear norm could not approximate the rank operator properly. In this paper, we propose a novel salient object detection method called Fast Iterative Truncated Nuclear Norm Recovery (FIT) to detect salient objects. Recent proposed Truncated Nuclear Norm is used as a convex relaxation of the rank operator, which consequently guarantees a higher accuracy while reducing time consumption in saliency detection. Series of experiments have been conducted on widely used public database. The results demonstrate the efficiency of our proposed algorithm compared with the state-of-the-art. © 2013 Springer-Verlag Berlin Heidelberg. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-42057-3-31 | IScIDE |
Keywords | Field | DocType |
low-rank matrix recovery,salient object detection,truncated nuclear norm | Computer vision,Salient object detection,Subspace topology,Pattern recognition,Salience (neuroscience),Computer science,Image segmentation,Matrix norm,Artificial intelligence,Operator (computer programming),Cognitive neuroscience of visual object recognition,Salient | Conference |
Volume | Issue | ISSN |
8261 LNCS | null | 16113349 |
Citations | PageRank | References |
1 | 0.36 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chuhang Zou | 1 | 1 | 0.36 |
| Yao Hu | 2 | 43 | 17.26 |
| Deng Cai | 3 | 7938 | 320.26 |
| Xiaofei He | 4 | 9139 | 386.38 |