@inproceedings{d3a9968f94f64a87832b75c454d6b9cc,
title = "Group saliency propagation for large scale and quick image co-segmentation",
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.",
keywords = "co-segmentation, fusion, group, ImageNet, large scale, propagation, quick",
author = "Jerripothula, \{Koteswar Rao\} and Jianfei Cai and Junsong Yuan",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE International Conference on Image Processing, ICIP 2015 ; Conference date: 27-09-2015 Through 30-09-2015",
year = "2015",
month = dec,
day = "9",
doi = "10.1109/ICIP.2015.7351686",
language = "English",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "4639--4643",
booktitle = "2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings",
address = "United States",
}