Updated 09 Sep 2018
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.
Koteswar Rao Jerripothula (2021). Matlab code Group-Saliency-Propagation-for-Co-segmentation (https://github.com/jkoteswarrao/Group-Saliency-Propagation-for-Large-scale-and-Quick-Image-Co-segmentation), GitHub. Retrieved .
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