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Promoting and Enhancing Reuse of Information throughout the Content Lifecycle taking account of Evolving Semantics
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SALIC: Social Active Learning for Image Classification - IEEE Transactions on Multimedia on 13/01/2017

In this paper, we present SALIC, an active learning method for selecting the most appropriate user tagged images to expand the training set of a binary classifier. The process of active learning can be fully automated in this social context by replacing the human oracle with the images' tags. However, their noisy nature adds further complexity to the sample selection process since, apart from the images' informativeness (i.e., how much they are expected to inform the classifier if we knew their label), our confidence about their actual label should also be maximized (i.e., how certain the oracle is on the images' true contents).

This paper has been published in the Journal: IEEE Transactions on Multimedia: http://ieeexplore.ieee.org/document/7466862/

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