This paper presents a multi-modal two-level framework for news story segmentation designed to cope with large news video corpus such as the data used in TREC video retrieval (TRECVID) evaluations. We divide our system into two levels: shot level that assigns one of the pre-defined semantic tags to each input shot; and story level that performs story segmentation based on the output of the shot level and other temporal features. We demonstrate the generality of our framework by employing two machine-learning approaches at the story level. The first approach employs a statistical method called Hidden Markov Models (HMM) whereas the second uses a rule induction technique. We tested both approaches on ~ 120 hours of news video provided by TRECVID 2003. The results demonstrate that our 2-level machine-learning framework is effective and is adequate to cope with large-scale practical problems.
KEYWORDS: Video, Wavelets, Video processing, Wavelet transforms, Algorithm development, Temporal resolution, Cameras, Convolution, Detection and tracking algorithms, RGB color model
Video segmentation is an important step in many of the video applications. We observe that the video shot boundary is a multi-resolution edge phenomenon in the feature space. Based on this observation, we have developed a novel temporal multi-resolution analysis (TMRA) based algorithm using Canny wavelets to perform temporal video segmentation. Information across multiple resolution is used to help detect as well as locate abrupt and gradual transitions. We present the theoretical basis of the algorithm followed by the implementation as well as the result. In this paper the TMRA technique has been implemented using color histogram in the raw domain and DCT coefficients in the compressed video streams as the feature space. Experimental result shows that this method can detect as well as characterize both the abrupt and gradual shot boundaries. The technique also shows good noise tolerance characteristics.
Conference Committee Involvement (10)
Mobile Devices and Multimedia: Enabling Technologies, Algorithms, and Applications 2015
10 February 2015 | San Francisco, California, United States
Mobile Devices and Multimedia: Enabling Technologies, Algorithms, and Applications 2014
3 February 2014 | San Francisco, California, United States
Multimedia Content Access: Algorithms and Systems VII
4 February 2013 | Burlingame, California, United States
Multimedia Content Access: Algorithms and Systems VI
23 January 2012 | Burlingame, California, United States
Multimedia Content Access: Algorithms and Systems V
25 January 2011 | San Francisco Airport, California, United States
Multimedia Content Access: Algorithms and Systems IV
21 January 2010 | San Jose, California, United States
Multimedia Content Access: Algorithms and Systems III
21 January 2009 | San Jose, California, United States
Multimedia Content Access: Algorithms and Systems II
30 January 2008 | San Jose, California, United States
Multimedia Content Access: Algorithms and Systems
31 January 2007 | San Jose, CA, United States
Multimedia Content Analysis, Management, and Retrieval 2006
18 January 2006 | San Jose, California, United States
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