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