Name
Title | ||
|---|---|---|
An adaptive transfer scheme based on sparse representation for figure-ground segmentation |
Abstract | ||
|---|---|---|
Figure-ground segmentation benefits lots of tasks in the field of computer vision. Exemplar-based approaches are capable of performing segmenting automatically without user interaction. However, most of them adopt fixed parameters for all the target images, which blocks their segmentation performances. We present a novel sparse representation based transfer scheme to gain adaptive parameters automatically. The proposed scheme transfers the segmentation masks of some windows from training images to obtain the soft mask of the target window from any given test image, when the target window can be represented by the linear combination of those windows. On the challenging PASCAL VOC 2010 segmentation dataset, experimental results and comparisons with the state-of-the-art methods show the effectiveness of the proposed scheme. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICIP.2014.7025673 | ICIP |
Keywords | Field | DocType |
adaptive transfer scheme,image segmentation,transfer scheme,pascal voc 2010 segmentation dataset,computer vision,figure-ground segmentation,sparse representation | Computer vision,Scale-space segmentation,Market segmentation,Pattern recognition,Computer science,Segmentation,Sparse approximation,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Standard test image,Minimum spanning tree-based segmentation | Conference |
ISSN | Citations | PageRank |
1522-4880 | 0 | 0.34 |
References | Authors | |
19 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xianyan Wu | 1 | 0 | 1.69 |
| Qi Han | 2 | 139 | 30.38 |
| Xiamu Niu | 3 | 754 | 91.72 |