DocumentCode :
2323672
Title :
News Story Segmentation in Multiple Modalities
Author :
Poulisse, Gert-Jan ; Moens, Marie-Francine ; Dekens, Tomas
Author_Institution :
Dept. of Comput. Sci., Katholieke Univ. Leuven, Leuven
fYear :
2009
fDate :
3-5 June 2009
Firstpage :
25
Lastpage :
32
Abstract :
In this paper, we describe an approach to segmenting news video based on the perceived shift in content using features spanning multiple modalities.We investigate a number of multimedia features, which serve as potential indicators of a change in story in order to determine which are the most effective. The efficacy of our approach is demonstrated by the performance of our prototype, where a number of feature combinations demonstrate an up to 18% improvement in Window Diff score above that of other state of the art story segmenters. In our investigation, there was no, one, clearly superior feature, rather the best segmentation results occurred when there was synergy between multiple features.
Keywords :
feature extraction; image segmentation; multimedia computing; video signal processing; art story segmenter; feature spanning multiple modality; multimedia feature; news video story segmentation; video signal processing; Broadcasting; Detectors; Entropy; Error analysis; Gunshot detection systems; Image segmentation; Indexing; Prototypes; Speech; Testing; feature extraction; story detection; video segmentation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Content-Based Multimedia Indexing, 2009. CBMI '09. Seventh International Workshop on
Conference_Location :
Chania
Print_ISBN :
978-1-4244-4265-2
Electronic_ISBN :
978-0-7695-3662-0
Type :
conf
DOI :
10.1109/CBMI.2009.27
Filename :
5137811
Link To Document :
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