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Learning object categories from Google's image search

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4 Author(s)
Fergus, R. ; Dept. of Eng. Sci., Oxford Univ. ; Fei-Fei, L. ; Perona, P. ; Zisserman, A.

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

Published in:

Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on  (Volume:2 )

Date of Conference:

17-21 Oct. 2005

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