Unsupervised summarization of rushes videos

Authors:
Yang Liu;Feng Zhou;Wei Liu;Fernando De la Torre;Yan Liu
Affiliations:
The Hong Kong Polytechnic University, Hong Kong, China;Carnegie Mellon University, Pittsburgh, PA, USA;Carnegie Mellon University, Pittsburgh, PA, USA;Carnegie Mellon University, Pittsburgh, PA, USA;The Hong Kong Polytechnic University, Hong Kong, China
Venue:
Proceedings of the international conference on Multimedia
Year:
2010

Citing 7
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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 effciently solved via dynamic programming. Experimental results on the TRECVID 2007 and 2008 BBC rushes video summarization databases validate the accuracy and effectiveness of CACA.