|
Published Articles >> Table of Contents >> Abstract
Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 2
pp. 1816-1823
Learning Object Categories from Google’s Image Search
R. Fergus, University of Oxford
L. Fei-Fei, California Institute of Technology
P. Perona, California Institute of Technology
A. Zisserman, University of Oxford
Full Article Text:
 
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICCV.2005.142
Send link to a friend
| 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 the models on standard test
sets, showing performance competitive with existing methods
trained on hand prepared datasets.
|
 Additional Information
|
Citation:
R. Fergus, L. Fei-Fei, P. Perona, A. Zisserman,
"Learning Object Categories from Google’s Image Search,"
iccv,
pp. 1816-1823,
Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 2,
2005
|
|
