Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/670013
Title: Learning object categories from Google's image search
Authors: Fergus, Robert;Fei-Fei, Li;Perona, Pietro;Zisserman, Andrew
subject: machine vision
Year: 2005
Abstract: Current approaches to object category recognition require datasets of training images to be manually prepared, with varying degrees of supervision. We present an approach that can learn an object category from just its name, by utilizing the raw output of image search engines available on the Internet. We develop a new model, TSI-pLSA, which extends pLSA (as applied to visual words) to include spatial information in a translation and scale invariant manner. Our approach can handle the high intra-class variability and large proportion of unrelated images returned by search engines. We evaluate tire models on standard test sets, showing performance competitive with existing methods trained on hand prepared datasets
Description: 
URI: http://eprints.pascal-network.org/archive/00001128/
http://localhost/handle/Hannan/342890
http://localhost/handle/Hannan/670013
Appears in Collections:2002-2008

Files in This Item:
File SizeFormat 
AL564270.pdf1.84 MBAdobe PDF
Title: Learning object categories from Google's image search
Authors: Fergus, Robert;Fei-Fei, Li;Perona, Pietro;Zisserman, Andrew
subject: machine vision
Year: 2005
Abstract: Current approaches to object category recognition require datasets of training images to be manually prepared, with varying degrees of supervision. We present an approach that can learn an object category from just its name, by utilizing the raw output of image search engines available on the Internet. We develop a new model, TSI-pLSA, which extends pLSA (as applied to visual words) to include spatial information in a translation and scale invariant manner. Our approach can handle the high intra-class variability and large proportion of unrelated images returned by search engines. We evaluate tire models on standard test sets, showing performance competitive with existing methods trained on hand prepared datasets
Description: 
URI: http://eprints.pascal-network.org/archive/00001128/
http://localhost/handle/Hannan/342890
http://localhost/handle/Hannan/670013
Appears in Collections:2002-2008

Files in This Item:
File SizeFormat 
AL564270.pdf1.84 MBAdobe PDF
Title: Learning object categories from Google's image search
Authors: Fergus, Robert;Fei-Fei, Li;Perona, Pietro;Zisserman, Andrew
subject: machine vision
Year: 2005
Abstract: Current approaches to object category recognition require datasets of training images to be manually prepared, with varying degrees of supervision. We present an approach that can learn an object category from just its name, by utilizing the raw output of image search engines available on the Internet. We develop a new model, TSI-pLSA, which extends pLSA (as applied to visual words) to include spatial information in a translation and scale invariant manner. Our approach can handle the high intra-class variability and large proportion of unrelated images returned by search engines. We evaluate tire models on standard test sets, showing performance competitive with existing methods trained on hand prepared datasets
Description: 
URI: http://eprints.pascal-network.org/archive/00001128/
http://localhost/handle/Hannan/342890
http://localhost/handle/Hannan/670013
Appears in Collections:2002-2008

Files in This Item:
File SizeFormat 
AL564270.pdf1.84 MBAdobe PDF