Incorporating prior knowledge with weighted margin support vector machines
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Knowledge adaptation for ad hoc multimedia event detection with few exemplars
Proceedings of the 20th ACM international conference on Multimedia
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In this paper, a new method that exploits related videos for the problem of event detection is proposed, where related videos are videos that are closely but not fully associated with the event of interest. In particular, the Weighted Margin SVM formulation is modified so that related class observations can be effectively incorporated in the optimization problem. The resulting Relevance Degree SVM is especially useful in problems where only a limited number of training observations is provided, e.g., for the EK10Ex subtask of TRECVID MED, where only ten positive and ten related samples are provided for the training of a complex event detector. Experimental results on the TRECVID MED 2011 dataset verify the effectiveness of the proposed method.