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Image grouping in knowledge-rich domains is challenging, since domain knowledge and human expertise are key to transform image pixels into meaningful content. Manually marking and annotating images is not only laborintensive but also ineffective. Furthermore, most traditional machine learning approaches cannot bridge this gap for the absence of experts' input. We thus present an interactive machine learning paradigm that allows experts to become an integral part of the learning process. Thisdoi:10.1007/s41060-016-0021-2 dblp:journals/ijdsa/GuoYLACSH16 fatcat:rvf5qfb3vjbvvgctwexe63fyia