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Unsupervised learning of categorical segments in image collections

Andreetto, Marco and Zelnik-Manor, Lihi and Perona, Pietro (2008) Unsupervised learning of categorical segments in image collections. In: Computer Vision and Pattern Recognition Workshops, 2008. Proceedings – IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE , pp. 158-165. ISBN 978-1-4244-2339-2.

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Which one comes first: segmentation or recognition? We propose a probabilistic framework for carrying out the two simultaneously. The framework combines an LDA ‘bag of visual words’ model for recognition, and a hybrid parametric-nonparametric model for segmentation. If applied to a collection of images, our framework can simultaneously discover the segments of each image, and the correspondence between such segments. Such segments may be thought of as the ‘parts’ of corresponding objects that appear in the image collection. Thus, the model may be used for learning new categories, detecting/classifying objects, and segmenting images.

Item Type:Book Section
Related URLs:
Perona, Pietro0000-0002-7583-5809
Additional Information:© 2008 IEEE. Funding for this research was provided by ONR-MURI Grant N00014-06-1-0734. Lihi Zelnik-Manor is supported by FP7-IRG grant 2009783.
Funding AgencyGrant Number
Office of Naval Research - Multidisciplinary University Research Initiative (ONR-MURI)N00014-06-1-0734
FP7-IRG grant2009783
Series Name:Proceedings – IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Record Number:CaltechAUTHORS:20100622-150143532
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:18763
Deposited By: Tony Diaz
Deposited On:09 Jul 2010 16:51
Last Modified:03 Oct 2019 01:47

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