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A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition

Fergus, R. and Perona, P. and Zisserman, A. (2005) A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE , Los Alamitos, CA , pp. 380-387. ISBN 0-7695-2372-2.

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We present a "parts and structure" model for object category recognition that can be learnt efficiently and in a semi-supervised manner: the model is learnt from example images containing category instances, without requiring segmentation from background clutter. The model is a sparse representation of the object, and consists of a star topology configuration of parts modeling the output of a variety of feature detectors. The optimal choice of feature types (whose repertoire includes interest points, curves and regions) is made automatically. In recognition, the model may be applied efficiently in an exhaustive manner, bypassing the need for feature detectors, to give the globally optimal match within a query image. The approach is demonstrated on a wide variety of categories, and delivers both successful classification and localization of the object within the image.

Item Type:Book Section
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Perona, P.0000-0002-7583-5809
Additional Information:© 2005 IEEE. Issue Date: 20-25 June 2005. Date of Current Version: 25 July 2005. We are very grateful for suggestions from and discussions with Michael Isard, Dan Huttenlocher and Alex Holub. Financial support was provided by: EC Project CogViSys; EC PASCAL Network of Excellence, IST-2002-506778; UK EPSRC; Caltech CNSE and the NSF.
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EC PASCAL Network of ExcellenceIST-2002-506778
Caltech Center for Neuromorphic Systems Engineering (CNSE)UNSPECIFIED
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INSPEC Accession Number8588898
Record Number:CaltechAUTHORS:20110817-083145183
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Official Citation:Fergus, R.; Perona, P.; Zisserman, A.; , "A sparse object category model for efficient learning and exhaustive recognition," Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on , vol.1, no., pp. 380- 387 vol. 1, 20-25 June 2005 doi: 10.1109/CVPR.2005.47 URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:24900
Deposited By: Ruth Sustaita
Deposited On:17 Aug 2011 15:59
Last Modified:03 Oct 2019 03:00

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