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A visual category filter for Google images

Fergus, Robert and Perona, Pietro and Zisserman, Andrew (2004) A visual category filter for Google images. In: Computer Vision - ECCV 2004. Lecture Notes in Computer Science. Vol.1. No.3021. Springer , Berlin, pp. 242-256. ISBN 978-3-540-21984-2.

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We extend the constellation model to include heterogeneous parts which may represent either the appearance or the geometry of a region of the object. The pans and their spatial configuration are learnt simultaneously and automatically, without supervision, from cluttered images. We describe how this model can be employed for ranking the output of an image search engine when searching for object categories. It is shown that visual consistencies in the output images can be identified, and then used to rank the images according to their closeness to the visual object category. Although the proportion of good images may be small, the algorithm is designed to be robust and is capable of learning in either a totally unsupervised manner, or with a very limited amount of supervision. We demonstrate the method on image sets returned by Google's image search for a number of object categories including bottles, camels, cars, horses, tigers and zebras.

Item Type:Book Section
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URLURL TypeDescription ReadCube access
Perona, Pietro0000-0002-7583-5809
Additional Information:© 2004 Springer. Financial support was provided by: EC Project CogViSys; UK EPSRC; Caltech CNSE and the NSF.
Funding AgencyGrant Number
Engineering and Physical Sciences Research Council (EPSRC)UNSPECIFIED
Center for Neuromorphic Systems Engineering, CaltechUNSPECIFIED
European Research Council (ERC)UNSPECIFIED
Series Name:Lecture Notes in Computer Science
Issue or Number:3021
Record Number:CaltechAUTHORS:20140730-101718931
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:47611
Deposited By: Caroline Murphy
Deposited On:19 Aug 2014 22:14
Last Modified:30 Jan 2020 23:59

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