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Proceedings Paper

Video genre classification using multimodal features
Author(s): Sung Ho Jin; Tae Meon Bae; Jin Ho Choo; Yong Man Ro
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Paper Abstract

We propose a video genre classification method using multimodal features. The proposed method is applied for the preprocessing of automatic video summarization or the retrieval and classification of broadcasting video contents. Through a statistical analysis of low-level and middle-level audio-visual features in video, the proposed method can achieve good performance in classifying several broadcasting genres such as cartoon, drama, music video, news, and sports. In this paper, we adopt MPEG-7 audio-visual descriptors as multimodal features of video contents and evaluate the performance of the classification by feeding the features into a decision tree-based classifier which is trained by CART. The experimental results show that the proposed method can recognize several broadcasting video genres with a high accuracy and the classification performance with multimodal features is superior to the one with unimodal features in the genre classification.

Paper Details

Date Published: 18 December 2003
PDF: 12 pages
Proc. SPIE 5307, Storage and Retrieval Methods and Applications for Multimedia 2004, (18 December 2003); doi: 10.1117/12.526252
Show Author Affiliations
Sung Ho Jin, Information and Communications Univ. (South Korea)
Tae Meon Bae, Information and Communications Univ. (South Korea)
Jin Ho Choo, Information and Communications Univ. (South Korea)
Yong Man Ro, Information and Communications Univ. (South Korea)


Published in SPIE Proceedings Vol. 5307:
Storage and Retrieval Methods and Applications for Multimedia 2004
Minerva M. Yeung; Rainer W. Lienhart; Chung-Sheng Li, Editor(s)

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