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 mutual information in multi-view video records. In this paper, we present a multiview metric learning framework for multi-view video summarization. It c...

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Bibliographic Details
Published in2016 International Conference on Cyberworlds (CW) pp. 179 - 182
Main Authors Linbo Wang, Xianyong Fang, Yanwen Guo, Yanwei Fu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2016
Subjects
Eigenvalues and eigenfunctions
Extraterrestrial measurements
Manifolds
Maximum margin clustering
Minimization
Multi-view metric learning
Multi-view video summarization
Optimization
Visualization
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Summary:Traditional methods on video summarization are designed to generate summaries for single-view video records, and thus they cannot fully exploit the mutual information in multi-view video records. In this paper, we present a multiview metric learning framework for multi-view video summarization. It 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 input data, and meanwhile to force the learned metric to maintain underlying intrinsic structure of data points, for example geometric information. Facilitated by such a framework, a systematic solution to the multi-view video summarization problem is developed from the viewpoint of metric learning. The effectiveness of the proposed method is demonstrated by experiments.
DOI:10.1109/CW.2016.38