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Multi-view Metric Learning for Multi-view Video Summarization

Traditional methods on video summarization are designed to generate summaries for single-view video records; and thus they cannot fully exploit the redundancy in multi-view video records. In this paper, we present a multi-view metric learning framework for multi-view video summarization that combines the advantages of maximum margin clustering with the disagreement minimization criterion. The learning framework thus has the ability to find a metric that best separates the data, and meanwhile to force the learned metric to maintain original intrinsic information between data points, for example geometric information. Facilitated by such a framework, a systematic solution to the multi-view video summarization problem is developed. To the best of our knowledge, it is the first time to address multi-view video summarization from the viewpoint of metric learning. The effectiveness of the proposed method is demonstrated by experiments.