Li, Fei-Fei and Perona, Pietro (2005) A Bayesian Hierarchical Model for Learning Natural Scene Categories. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE , Los Alamitos, CA, pp. 524-531. ISBN 0-7695-2372-2 http://resolver.caltech.edu/CaltechAUTHORS:20110809-110622441
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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.
|Item Type:||Book Section|
|Additional Information:||© 2005 IEEE. Issue Date: 20-25 June 2005. Date of Current Version: 25 July 2005. We would like to thank Chris Bishop, Tom Minka, Silvio Savarese and Max Welling for helpful discussions. We also thank Aude Oliva and Michael Fink for providing parts of the dataset.|
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|Official Citation:||Fei-Fei, L.; Perona, P.; , "A Bayesian hierarchical model for learning natural scene categories," Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on , vol.2, no., pp. 524- 531 vol. 2, 20-25 June 2005 doi: 10.1109/CVPR.2005.16 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1467486&isnumber=31473|
|Usage Policy:||No commercial reproduction, distribution, display or performance rights in this work are provided.|
|Deposited By:||Tony Diaz|
|Deposited On:||12 Sep 2011 16:39|
|Last Modified:||23 Aug 2016 10:04|
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