Object Co-Labeling in Multiple Images
Abstract
We introduce a new problem called object co-labeling where the goal is to jointly annotate multiple images of the same scene which do not have temporal consistency. We present an adaptive framework for joint segmentation and recognition to solve this problem. We propose an objective function that considers not only appearance but also appearance and context consistency across images of the scene. A relaxed form of the cost function is minimized using an efficient quadratic programming solver. Our approach improves labeling performance compared to labeling each image individually. We also show the application of our co-labeling framework to other recognition problems such as label propagation in videos and object recognition in similar scenes. Experimental results demonstrates the efficacy of our approach.
Cite
Text
Chen et al. "Object Co-Labeling in Multiple Images." IEEE/CVF Winter Conference on Applications of Computer Vision, 2014. doi:10.1109/WACV.2014.6836031Markdown
[Chen et al. "Object Co-Labeling in Multiple Images." IEEE/CVF Winter Conference on Applications of Computer Vision, 2014.](https://mlanthology.org/wacv/2014/chen2014wacv-object/) doi:10.1109/WACV.2014.6836031BibTeX
@inproceedings{chen2014wacv-object,
title = {{Object Co-Labeling in Multiple Images}},
author = {Chen, Xi and Jain, Arpit and Davis, Larry S.},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2014},
pages = {721-728},
doi = {10.1109/WACV.2014.6836031},
url = {https://mlanthology.org/wacv/2014/chen2014wacv-object/}
}