Automatic Visual Concept Learning for Social Event Understanding Vision-based event analysis is extremely difficult due to the various concepts (object, action, and scene) contained in videos. Though visual concept-based event analysis has achieved significant progress, it has two disadvantages: visual concept is defined manually, and has only one corresponding classifier in traditional methods. To deal with these issues, we propose a novel automatic visual concept learning algorithm for social event understanding in videos. First, instead of defining visual concept manually, we propose an effective automatic concept mining algorithm with the help of Wikipedia, N-gram Web services, and Flickr. Then, based on the learned visual concept, we propose a novel boosting concept learning algorithm to iteratively learn multiple classifiers for each concept to enhance its representative discriminability. The extensive experimental evaluations on the collected dataset well demonstrate the effectiveness of the proposed algorithm for social event understanding.