A bayesian hierarchical model for learning natural scene categories (2005)
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| Venue: | In CVPR |
| Citations: | 321 - 11 self |
BibTeX
@INPROCEEDINGS{Fei-fei05abayesian,
author = {Li Fei-fei},
title = {A bayesian hierarchical model for learning natural scene categories},
booktitle = {In CVPR},
year = {2005},
pages = {524--531}
}
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Abstract
We propose a novel approach to learn and recognize natural scene categories. Unlike previous work [9, 17], 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. 1.
