Latent Topic Random Fields: Learning Using a Taxonomy of Labels
Abstract
An important problem in image labeling concerns learning with images labeled at varying levels of specificity. We propose an approach that can incorporate images with labels drawn from a semantic hierarchy, and can also readily cope with missing labels, and roughly-specified object boundaries. We introduce a new form of latent topic model, learning a novel context representation in the joint label-and-image space by capturing co-occurring patterns within and between image features and object labels. Given a topic, the model generates the input data, as well as a topic-dependent probabilistic classifier to predict labels for image regions. We present results on two real-world datasets, demonstrating significant improvements gained by including the coarsely labeled images.
Cite
Text
He and Zemel. "Latent Topic Random Fields: Learning Using a Taxonomy of Labels." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008. doi:10.1109/CVPR.2008.4587362Markdown
[He and Zemel. "Latent Topic Random Fields: Learning Using a Taxonomy of Labels." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008.](https://mlanthology.org/cvpr/2008/he2008cvpr-latent/) doi:10.1109/CVPR.2008.4587362BibTeX
@inproceedings{he2008cvpr-latent,
title = {{Latent Topic Random Fields: Learning Using a Taxonomy of Labels}},
author = {He, Xuming and Zemel, Richard S.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2008},
doi = {10.1109/CVPR.2008.4587362},
url = {https://mlanthology.org/cvpr/2008/he2008cvpr-latent/}
}