Abbasnejad, M.E.Dick, A.R.Shi, Q.Hengel, A.V.D.2018-11-082018-11-082018Proceedings of BMVC 2018 and Workshops, 2018, pp.1-13http://hdl.handle.net/2440/115918Learning successful image classification models requires large quantities of labelled examples that are generally hard to obtain. On the other hand, the web provides an abundance of loosely labelled images, i.e. tagged in websites such as Flickr. Although these images are cheap and massively available, their tags are typically noisy and unreliable. In an attempt to use such images for training a classifier, we propose a simple probabilistic model to learn a latent semantic space from which deep vector representations of the images and tags are generated. This latent space is subsequently used in an active learning framework based on adaptive submodular optimisation that selects informative images to be labelled. Afterwards, we update the classifier according to the importance of each labelled image to best capture the information they provide. Through this simple approach, we are able to train a classifier that performs well using a fraction of the effort that is typically required for image labelling and classifier training.en© 2018. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.Conference paper00300990272-s2.0-85072337942439583Dick, A.R. [0000-0001-9049-7345]Shi, Q. [0000-0002-9126-2107]Hengel, A.V.D. [0000-0003-3027-8364]