A bayesian hierarchical model for learning natural scene categories

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A bayesian hierarchical model for learning natural scene categories (2005)

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by Li Fei-fei

Venue:	In CVPR
Citations:	946 - 15 self

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@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.

Keyphrases

natural scene category bayesian hierarchical model previous work theme distribution training set unsupervised learning codewords distribution satisfactory categorization performance large set novel approach complex scene local region

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