ML Anthology

A Bayesian Hierarchical Model for Learning Natural Scene Categories

Li Fei-Fei, Pietro Perona
CVPR 2005 pp. 524-531
doi:10.1109/CVPR.2005.16 /cvpr/2005/feifei2005cvpr-bayesian/

Abstract

We propose a novel approach to learn and recognize natural scene categories. Unlike previous work, it does not require experts to annotate the training set. We represent the image of a scene by a collection of local regions, denoted as codewords obtained by unsupervised learning. Each region is represented as part of a "theme". In previous work, such themes were learnt from hand-annotations of experts, while our method learns the theme distributions as well as the codewords distribution over the themes without supervision. We report satisfactory categorization performances on a large set of 13 categories of complex scenes.

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Cite

Text

Fei-Fei and Perona. "A Bayesian Hierarchical Model for Learning Natural Scene Categories." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005. doi:10.1109/CVPR.2005.16

Markdown

[Fei-Fei and Perona. "A Bayesian Hierarchical Model for Learning Natural Scene Categories." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005.](https://mlanthology.org/cvpr/2005/feifei2005cvpr-bayesian/) doi:10.1109/CVPR.2005.16

BibTeX

@inproceedings{feifei2005cvpr-bayesian,
  title     = {{A Bayesian Hierarchical Model for Learning Natural Scene Categories}},
  author    = {Fei-Fei, Li and Perona, Pietro},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2005},
  pages     = {524-531},
  doi       = {10.1109/CVPR.2005.16},
  url       = {https://mlanthology.org/cvpr/2005/feifei2005cvpr-bayesian/}
}