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The huge amount of time required to construct a set of labeled images to train a classifier has led researchers to develop algorithms which can identify the most informative training images, such that labelling those will be sufficient to achieve a considerable classification accuracy. In this paper we focus on choosing a subset of the most informative and diverse images based on which the classification model can be learned efficiently. The size of the subset to be chosen is determined by thedoi:10.1109/icip.2016.7532406 dblp:conf/icip/PaulBR16 fatcat:ltfrtmo3cjev3awyadqyzwfp4e