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ICCV
2005
IEEE

Learning Object Categories from Google's Image Search

9 years 11 months ago
Learning Object Categories from Google's Image Search
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.
Robert Fergus, Fei-Fei Li 0002, Pietro Perona, And
Added 15 Oct 2009
Updated 15 Oct 2009
Type Conference
Year 2005
Where ICCV
Authors Robert Fergus, Fei-Fei Li 0002, Pietro Perona, Andrew Zisserman
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