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Learning by expansion: Exploiting social media for image classification with few training examples
2012
Neurocomputing
Witnessing the sheer amount of user-contributed photos and videos, we argue to leverage such freely available image collections as the training images for image classification. We propose an image expansion framework to mine more semantically related training images from the auxiliary image collection provided with very few training examples. The expansion is based on a semantic graph considering both visual and (noisy) textual similarities in the auxiliary image collections, where we also
doi:10.1016/j.neucom.2011.05.043
fatcat:uyv5apblujasnkk36cegz2faky