Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs
Abstract
We introduce the term cosegmentation which denotes the task of segmenting simultaneously the common parts of an image pair. A generative model for cosegmentation is presented. Inference in the model leads to minimizing an energy with an MRF term encoding spatial coherency and a global constraint which attempts to match the appearance histograms of the common parts. This energy has not been proposed previously and its optimization is challenging and NP-hard. For this problem a novel optimization scheme which we call trust region graph cuts is presented. We demonstrate that this framework has the potential to improve a wide range of research: Object driven image retrieval, video tracking and segmentation, and interactive image editing. The power of the framework lies in its generality, the common part can be a rigid/non-rigid object (or scene), observed from different viewpoints or even similar objects of the same class.
Cite
Text
Rother et al. "Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2006. doi:10.1109/CVPR.2006.91Markdown
[Rother et al. "Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2006.](https://mlanthology.org/cvpr/2006/rother2006cvpr-cosegmentation/) doi:10.1109/CVPR.2006.91BibTeX
@inproceedings{rother2006cvpr-cosegmentation,
title = {{Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs}},
author = {Rother, Carsten and Minka, Thomas P. and Blake, Andrew and Kolmogorov, Vladimir},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2006},
pages = {993-1000},
doi = {10.1109/CVPR.2006.91},
url = {https://mlanthology.org/cvpr/2006/rother2006cvpr-cosegmentation/}
}