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A Bayesian hierarchical model for learning natural scene categories

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2 Author(s)
Fei-Fei, L. ; Dept. of Electr. Eng., California Inst. of Technol., Pasadena, CA, USA ; Perona, P.

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.

Published in:

Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on  (Volume:2 )

Date of Conference:

20-25 June 2005