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Proceedings Paper

Video shot classification with concept detection
Author(s): Zhong Ji; Yuting Su
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Paper Abstract

It is a challenging work to classify video shots into a predefined genre set according to their semantic contents, which is helpful to video indexing, summarization and retrieval. This research proposes a novel shot classification algorithm with concept detection for news video programs. Six semantic shot types are studied and categorized: Anchorperson, Monologue, Reporter, Commercial, Still image and Miscellaneous, in which anchorperson shots are detected by clustering methods, reporter and monologue shots are distinguished by Conditional Random Fields (CRFs), and the last three categories are picked out by rule-based methods. Multimodality features are employed, such as visual, audio, face, temporal and contextual features. The experimental results show its effectiveness and achieve a high average accuracy of 96.5%.

Paper Details

Date Published: 15 November 2007
PDF: 7 pages
Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 678816 (15 November 2007); doi: 10.1117/12.749160
Show Author Affiliations
Zhong Ji, Tianjin Univ. (China)
Yuting Su, Tianjin Univ. (China)

Published in SPIE Proceedings Vol. 6788:
MIPPR 2007: Pattern Recognition and Computer Vision
S. J. Maybank; Mingyue Ding; F. Wahl; Yaoting Zhu, Editor(s)

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