A Hierarchical Conditional Random Field Model for Labeling and Segmenting Images of Street Scenes
Abstract
Simultaneously segmenting and labeling images is a fundamental problem in Computer Vision. In this paper, we introduce a hierarchical CRF model to deal with the problem of labeling images of street scenes by several distinctive object classes. In addition to learning a CRF model from all the labeled images, we group images into clusters of similar images and learn a CRF model from each cluster separately. When labeling a new image, we pick the closest cluster and use the associated CRF model to label this image. Experimental results show that this hierarchical image labeling method is comparable to, and in many cases superior to, previous methods on benchmark data sets. In addition to segmentation and labeling results, we also showed how to apply the image labeling result to rerank Google similar images.
Cite
Text
Huang et al. "A Hierarchical Conditional Random Field Model for Labeling and Segmenting Images of Street Scenes." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011. doi:10.1109/CVPR.2011.5995571Markdown
[Huang et al. "A Hierarchical Conditional Random Field Model for Labeling and Segmenting Images of Street Scenes." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011.](https://mlanthology.org/cvpr/2011/huang2011cvpr-hierarchical/) doi:10.1109/CVPR.2011.5995571BibTeX
@inproceedings{huang2011cvpr-hierarchical,
title = {{A Hierarchical Conditional Random Field Model for Labeling and Segmenting Images of Street Scenes}},
author = {Huang, Qi-Xing and Han, Mei and Wu, Bo and Ioffe, Sergey},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2011},
pages = {1953-1960},
doi = {10.1109/CVPR.2011.5995571},
url = {https://mlanthology.org/cvpr/2011/huang2011cvpr-hierarchical/}
}