Learning to Parse Images
Abstract
We describe a class of probabilistic models that we call credibility networks. Using parse trees as internal representations of images, credibility networks are able to perform segmentation and recog(cid:173) nition simultaneously, removing the need for ad hoc segmentation heuristics. Promising results in the problem of segmenting hand(cid:173) written digits were obtained.
Cite
Text
Hinton et al. "Learning to Parse Images." Neural Information Processing Systems, 1999.Markdown
[Hinton et al. "Learning to Parse Images." Neural Information Processing Systems, 1999.](https://mlanthology.org/neurips/1999/hinton1999neurips-learning/)BibTeX
@inproceedings{hinton1999neurips-learning,
title = {{Learning to Parse Images}},
author = {Hinton, Geoffrey E. and Ghahramani, Zoubin and Teh, Yee Whye},
booktitle = {Neural Information Processing Systems},
year = {1999},
pages = {463-469},
url = {https://mlanthology.org/neurips/1999/hinton1999neurips-learning/}
}