Name
Abstract | ||
|---|---|---|
Superpixel segmentation has been widely used in many computer vision tasks. Existing superpixel algorithms are mainly based on hand-crafted features, which often fail to preserve weak object boundaries. In this work, we leverage deep neural networks to facilitate extracting superpixels from images. We show a simple integration of deep features with existing superpixel algorithms does not result in better performance as these features do not model segmentation. Instead, we propose a segmentation-aware affinity learning approach for superpixel segmentation. Specifically, we propose a new loss function that takes the segmentation error into account for affinity learning. We also develop the Pixel Affinity Net for affinity prediction. Extensive experimental results show that the proposed algorithm based on the learned segmentation-aware loss performs favorably against the state-of-the-art methods. We also demonstrate the use of the learned superpixels in numerous vision applications with consistent improvements. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/CVPR.2018.00066 | 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition |
Keywords | Field | DocType |
deep neural networks,superpixel extraction,deep feature integration,segmentation-aware affinity loss,learned superpixels,learned segmentation-aware loss performs,Pixel Affinity Net,segmentation error,loss function,segmentation-aware affinity learning approach,weak object boundaries,hand-crafted features,superpixel algorithms,computer vision tasks,superpixel segmentation | Computer vision,Pattern recognition,Task analysis,Segmentation,Computer science,Image segmentation,Feature extraction,Pixel,Artificial intelligence,Cluster analysis,Deep neural networks,Superpixel segmentation | Conference |
ISSN | ISBN | Citations |
1063-6919 | 978-1-5386-6421-6 | 7 |
PageRank | References | Authors |
0.41 | 18 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei-Chih Tu | 1 | 34 | 4.47 |
| Ming-Yu Liu | 2 | 872 | 35.44 |
| Varun Jampani | 3 | 184 | 19.44 |
| Deqing Sun | 4 | 1061 | 44.84 |
| Shao-Yi Chien | 5 | 1603 | 154.48 |
| Yang Ming-Hsuan | 6 | 15303 | 620.69 |
| Jan Kautz | 7 | 3615 | 198.77 |