Graduate School and Research Center in Digital Sciences

Multi-Video summarization based on AV-MMR

Li, Yingbo; Mérialdo, Bernard

CBMI 2010, 8th International Workshop on Content-Based Multimedia Indexing, June 23-25, 2010, Grenoble, France

  This paper presents an algorithm for video summarization, Audio Video Maximal Marginal Relevance (AV-MMR), exploiting both audio and video information. It is an extension of the Video Maximal Marginal Relevance (Video-MMR) algorithm which was only based on visual information. AV-MMR iteratively selects segments which best represent unselected information and are non redundant with previously selected information. As for Video-MMR, AV-MMR is a generic algorithm which is suitable for both single and multiple videos with multiple genres. Several variants of AV-MMR are proposed and the best one is identified by experimentation. Besides, a visual representation of the coherence of audio and video information for a set of audio-visual sequences is also proposed.

Document Doi Bibtex

Title:Multi-Video summarization based on AV-MMR
Department:Data Science
Eurecom ref:3079
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Bibtex: @inproceedings{EURECOM+3079, doi = { }, year = {2010}, title = {{M}ulti-{V}ideo summarization based on {AV}-{MMR}}, author = {{L}i, {Y}ingbo and {M}{\'e}rialdo, {B}ernard}, booktitle = {{CBMI} 2010, 8th {I}nternational {W}orkshop on {C}ontent-{B}ased {M}ultimedia {I}ndexing, {J}une 23-25, 2010, {G}renoble, {F}rance}, address = {{G}renoble, {FRANCE}}, month = {06}, url = {} }
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