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A Sparse Object Category Model for Efficient Learning and Complete Recognition

Fergus, Rob and Perona, Pietro and Zisserman, Andrew (2006) A Sparse Object Category Model for Efficient Learning and Complete Recognition. In: Toward Category-Level Object Recognition. Lecture Notes in Computer Science. No.4170. Springer , Berlin, Heidelberg, pp. 443-461. ISBN 9783540687948.

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We present a “parts and structure” model for object category recognition that can be learnt efficiently and in a weakly-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 a complete 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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URLURL TypeDescription
Perona, Pietro0000-0002-7583-5809
Additional Information:© Springer-Verlag Berlin Heidelberg 2006. 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.
Funding AgencyGrant Number
European Research Council (ERC)CogViSys
EC PASCAL Network of ExcellenceIST-2002-506778
Engineering and Physical Sciences Research Council (EPSRC)UNSPECIFIED
Center for Neuromorphic Systems Engineering, CaltechUNSPECIFIED
Series Name:Lecture Notes in Computer Science
Issue or Number:4170
Record Number:CaltechAUTHORS:20190328-144424435
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94255
Deposited By: George Porter
Deposited On:28 Mar 2019 22:54
Last Modified:03 Oct 2019 21:02

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