Abstract
Most of the existing co-segmentation methods are usually complex, and require pre-grouping of images, fine-tuning a few parameters and initial segmentation masks etc. These limitations become serious concerns for their application on large scale datasets. In this paper, Group Saliency Propagation (GSP) model is proposed where a single group saliency map is developed, which can be propagated to segment the entire group. In addition, it is also shown how a pool of these group saliency maps can help in quickly segmenting new input images. Experiments demonstrate that the proposed method can achieve competitive performance on several benchmark co-segmentation datasets including ImageNet, with the added advantage of speed up.
Original language | English |
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Title of host publication | 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings |
Editors | Jean-Philippe Thiran, Fabrice Labeau |
Place of Publication | Piscataway NJ USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 4639-4643 |
Number of pages | 5 |
ISBN (Electronic) | 9781479983391, 9781479983384 |
DOIs | |
Publication status | Published - 2015 |
Externally published | Yes |
Event | IEEE International Conference on Image Processing 2015 - Quebec City, Canada Duration: 27 Sept 2015 → 30 Sept 2015 Conference number: 22nd http://icip2015.org/index.html https://ieeexplore.ieee.org/xpl/conhome/7328364/proceeding (Proceedings) |
Conference
Conference | IEEE International Conference on Image Processing 2015 |
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Abbreviated title | ICIP 2015 |
Country/Territory | Canada |
City | Quebec City |
Period | 27/09/15 → 30/09/15 |
Internet address |
Keywords
- co-segmentation
- fusion
- group
- ImageNet
- large scale
- propagation
- quick