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Video rushes summarization using spectral clustering and sequence alignment

Published: 31 October 2008 Publication History

Abstract

In this paper we describe a system for video rushes summarization. The basic problems of rushes videos are three. First, the presence of useless frames such as colorbars, monochrome frames and frames containing clapboards. Second, the repetition of similar segments produced from multiple takes of the same scene and finally, the efficient representation of the original video in the video summary. In the method we proposed herein, the input video is segmented into shots. Then, colorbars and monochrome frames are removed by checking their edge direction histogram, whereas frames containing clapboards are removed by checking their SIFT descriptors. Next, an enhanced spectral clustering algorithm that both estimates the number of clusters and employs the fast global k-means algorithm in the clustering stage after the eigenvector computation of the similarity matrix is used to extract the key-frames of each shot, to efficiently represent shot content. Similar shots are clustered in one group by comparing their key-frames using a sequence alignment algorithm. Each group is represented from the shot with the largest duration and the final video summary is generated by concatenating frames around the key-frames of each shot. Experiments on TRECVID 2008 Test Data indicate that our method exhibits good performance.

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  • (2017)Novel Method for Storyboarding Biomedical Videos for Medical Informatics2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)10.1109/CBMS.2017.57(127-132)Online publication date: Jun-2017
  • (2015)Partial Least Squares Image ClusteringProceedings of the 2015 28th SIBGRAPI Conference on Graphics, Patterns and Images10.1109/SIBGRAPI.2015.25(41-48)Online publication date: 26-Aug-2015
  • (2014)Online Video Summarization Based on Local FeaturesInternational Journal of Multimedia Data Engineering and Management10.4018/ijmdem.20140401035:2(41-53)Online publication date: 1-Apr-2014
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cover image ACM Conferences
TVS '08: Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
October 2008
156 pages
ISBN:9781605583099
DOI:10.1145/1463563
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

Publication History

Published: 31 October 2008

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Author Tags

  1. global k-means
  2. key-frame extraction
  3. spectral clustering
  4. video summarization

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MM08
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MM08: ACM Multimedia Conference 2008
October 31, 2008
British Columbia, Vancouver, Canada

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Cited By

View all
  • (2017)Novel Method for Storyboarding Biomedical Videos for Medical Informatics2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)10.1109/CBMS.2017.57(127-132)Online publication date: Jun-2017
  • (2015)Partial Least Squares Image ClusteringProceedings of the 2015 28th SIBGRAPI Conference on Graphics, Patterns and Images10.1109/SIBGRAPI.2015.25(41-48)Online publication date: 26-Aug-2015
  • (2014)Online Video Summarization Based on Local FeaturesInternational Journal of Multimedia Data Engineering and Management10.4018/ijmdem.20140401035:2(41-53)Online publication date: 1-Apr-2014
  • (2014)Reducing redundancy in videos using reference frame and clustering technique of key frame extractionInternational Conference on Circuits, Communication, Control and Computing10.1109/CIMCA.2014.7057840(348-440)Online publication date: Nov-2014
  • (2014)Rushes Video Segmentation Using Semantic FeaturesArtificial Intelligence: Methods and Applications10.1007/978-3-319-07064-3_9(105-114)Online publication date: 2014
  • (2013)Video Segmentation and Structuring for Indexing ApplicationsMultimedia Data Engineering Applications and Processing10.4018/978-1-4666-2940-0.ch011(205-225)Online publication date: 2013
  • (2013)Online video summarization on compressed domainJournal of Visual Communication and Image Representation10.1016/j.jvcir.2012.01.00924:6(729-738)Online publication date: Aug-2013
  • (2013)A Classification-Based Approach for Retake and Scene Detection in Rushes VideoNeural Information Processing10.1007/978-3-642-42051-1_75(608-615)Online publication date: 2013
  • (2012)A Framework for Robust Online Video Contrast Enhancement Using Modularity OptimizationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2012.219813622:9(1266-1279)Online publication date: 1-Sep-2012
  • (2012)Key frame extraction from consumer videos using epitome2012 19th IEEE International Conference on Image Processing10.1109/ICIP.2012.6466803(93-96)Online publication date: Sep-2012
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