Multi-video summarization based on Video-MMR

Author

Li, Yingbo ; Merialdo, Bernard

Author_Institution

Inst. Eurecom, France

fYear

2010

fDate

12-14 April 2010

Firstpage

1

Lastpage

4

Abstract

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.

Keywords

content management; multimedia computing; text analysis; K-means algorithm; global summarization; individual video; multivideo summarization; redundant keyframe; text summarization; user-made summary; video maximal marginal relevance; Humans; Visualization;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Analysis for Multimedia Interactive Services (WIAMIS), 2010 11th International Workshop on

Conference_Location

Desenzano del Garda

Print_ISBN

978-1-4244-7848-4

Electronic_ISBN

978-88-905328-0-1

Type

conf

Filename

5617655