This paper presents a novel and effective approach for multi-video summarization: Video Maximal Marginal Relevance (Video-MMR), which extends a classical algorithm of text summarization, Maximal Marginal Relevance. Video-MMR rewards relevant keyframes and penalizes redundant keyframes, as MMR does with text fragments. Two variants of Video-MMR are suggested, and we propose a criterion to select the best combination of parameters for Video-MMR. Then, we compare two summarization strategies: Global Summarization, which summarizes all the individual videos at the same time, and Individual Summarization, which summarizes each individual video independently and concatenates the results. Finally, Video-MMR algorithm is compared with popular K-means algorithm, supported by user-made summary.
Multi-video summarization based on video-MMR
WIAMIS 2010, 11th International Workshop on Image Analysis for Multimedia Interactive Services, April 12-14, 2010, Desenzano del Garda, Italy
Type:
Conférence
City:
Desenzano del Garda
Date:
2010-04-12
Department:
Data Science
Eurecom Ref:
3026
Copyright:
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See also:
PERMALINK : https://www.eurecom.fr/publication/3026
