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Active learning has been demonstrated to be a useful tool to reduce human labeling effort for many multimedia applications, especially for those handling large video collections. However, most of the previous work on active learning has focused on only binary classification, which greatly limits the applicability of active learning. We present a multi-class active learning approach which extends active learning from binary classification to multi-class classification using a unifieddoi:10.1184/r1/6607544 fatcat:dqkvxrbi6bclreqslbxapyluvi