Unsupervised Summarization of Rushes Videos

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Abstract

This paper proposes a new framework to formulate summarization of rushes video as an unsupervised learning problem. We pose the problem of video summarization as one of time-series clustering, and proposed Constrained Aligned Cluster Analysis (CACA). CACA combines kernel k-means, Dynamic Time Alignment Kernel (DTAK), and unlike previous work, CACA jointly optimizes video segmentation and shot clustering. CACA is efficiently solved via dynamic programming. Experimental results on the TRECVID 2007 and 2008 BBC rushes video summarization databases validate the accuracy and effectiveness of CACA.

Citation

Yang Liu, Feng Zhou, Wei Liu, Fernando de la Torre and Yan Liu
Unsupervised Summarization of Rushes Videos
ACM Multimedia 2010
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Acknowledgements and Funding

The research and development of the AFERS application is supported by the Technical Support Working Group through funding from the Investigative Support and Forensics subgroup to Platinum Solutions, Inc. Thanks to Dr. Andrew Ryan from the Naval Criminal Investigative Service for his sponsorship of this initiative.

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