In this paper, we present our new results in news video story
segmentation and classification in the context of TRECVID video
retrieval benchmarking event 2003. We applied and extended the
Maximum Entropy statistical model to effectively fuse diverse
features from multiple levels and modalities, including visual,
audio, and text. We have included various features such as motion,
face, music/speech types, prosody, and high-level text
segmentation information. The statistical fusion model is used to
automatically discover relevant features contributing to the
detection of story boundaries. One novel aspect of our method is
the use of a feature wrapper to address different types of
features -- asynchronous, discrete, continuous and delta ones. We
also developed several novel features related to prosody. Using
the large news video set from the TRECVID 2003 benchmark, we
demonstrate satisfactory performance (F1 measures up to 0.76 in
ABC news and 0.73 in CNN news), present how these multi-level
multi-modal features construct the probabilistic framework, and
more importantly observe an interesting opportunity for further
improvement.
Conference Committee Involvement (5)
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
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