AbstractIn this paper, we describe a simple approach to learning models of visual object categories from images gathered from Internet image search engines. The images for a given keyword are typically highly variable, with a large fraction being unrelated to the query term, and thus pose a challenging environment from which to learn. By training our models directly from Internet images, we remove the need to laboriously compile training data sets, required by most other recognition approaches-this opens up the possibility of learning object category models “on-the-fly.” We describe two simple approaches, derived from the probabilistic latent semantic analysis (pLSA) technique for text document analysis, that can be used to automatically learn object models from these data. We show two applications of the learned model: first, to rerank the images returned by the search engine, thus improving the quality of the search engine; and second, to recognize objects in other image data sets.
Fergus, Rob and Li, Fei-Fei and Perona, Pietro and Zisserman, Andrew (2010) Learning Object Categories From Internet Image Searches. Proceedings of the IEEE, 98 (8). pp. 1453-1466. ISSN 0018-9219. http://resolver.caltech.edu/CaltechAUTHORS:20101116-100053561 <http://resolver.caltech.edu/CaltechAUTHORS:20101116-100053561>