Multimedia Tools and Applications, January 2016, Vol. 75, N°1, ISSN: 1380-7501
In this paper we propose several novel algorithms for multi-video summarization. The first and essential algorithm, Video Maximal Marginal Relevance (Video-MMR), mimics the principle of a classical algorithm of text summarization, Maximal Marginal Relevance (MMR). Video-MMR rewards relevant keyframes and penalizes redundant keyframes, only relying on visual features. We extend Video-MMR to Audio Video Maximal Marginal Relevance (AV-MMR) by exploiting audio features. We also propose Balanced AV-MMR, which exploits additional semantic features, the balance between audio information and visual information, and the balance of temporal information in different videos of a set. The proposed algorithms are generic and suitable for summarizing various video genres in multi-video set by using multimodal information. Our series of MMR algorithms for multi-video summarization are proved to be effective by the large-scale subjective and objective evaluation.
Type:
Journal
Date:
2016-01-12
Department:
Data Science
Eurecom Ref:
4416
Copyright:
© Springer. Personal use of this material is permitted. The definitive version of this paper was published in Multimedia Tools and Applications, January 2016, Vol. 75, N°1, ISSN: 1380-7501 and is available at : http://dx.doi.org/10.1007/s11042-014-2287-5
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