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Seventh IEEE International Conference on Advanced Learning Technologies (ICALT 2007)
VCSR: Video Content Summarization for Recommendation
Niigata, Japan
July 18-July 20
ISBN: 0-7695-2916-X
ASCII Text
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Chi-Cheng Tsai, Ching-I Chung, Yi-Ting Huang, Chia-Hsing Shen, Yu-Chieh Wu, Jie-Chi Yang, "VCSR: Video Content Summarization for Recommendation," Advanced Learning Technologies, IEEE International Conference on, pp. 862-864, Seventh IEEE International Conference on Advanced Learning Technologies (ICALT 2007), 2007.
 
 
BibTex
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@article{ 10.1109/ICALT.2007.279,
author = {Chi-Cheng Tsai and Ching-I Chung and Yi-Ting Huang and Chia-Hsing Shen and Yu-Chieh Wu and Jie-Chi Yang},
title = {VCSR: Video Content Summarization for Recommendation},
journal ={Advanced Learning Technologies, IEEE International Conference on},
volume = {0},
year = {2007},
isbn = {0-7695-2916-X},
pages = {862-864},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICALT.2007.279},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Advanced Learning Technologies, IEEE International Conference on
TI - VCSR: Video Content Summarization for Recommendation
SN - 0-7695-2916-X
SP862
EP864
A1 - Chi-Cheng Tsai,
A1 - Ching-I Chung,
A1 - Yi-Ting Huang,
A1 - Chia-Hsing Shen,
A1 - Yu-Chieh Wu,
A1 - Jie-Chi Yang,
PY - 2007
KW - null
VL - 0
JA - Advanced Learning Technologies, IEEE International Conference on
ER -
 
Chi-Cheng Tsai, National Central University, Taiwan
Ching-I Chung, National Central University, Taiwan
Yi-Ting Huang, National Central University, Taiwan
Chia-Hsing Shen, National Central University, Taiwan
Yu-Chieh Wu, National Central University, Taiwan
Jie-Chi Yang, National Central University, Taiwan
In this paper, the authors present a Video Content Summarization for Recommendation (called VCSR) system to auto-recommend suitable multimedia learning materials for learners. The VCSR system firstly extracts important content as summarization from input raw video data, while the generated summarization will be auto-routed to learners according to their profiles. Video captions are initially recognized using Optical Character Recognition (OCR), then a set of key passages with corresponding frame images are extracted to form a video summary. The recommendation is achieved by calculating the relevance of the video summarization for each learner. Also, this paper indicates how the VCSR system effectively plays the intermediate role in a modern digital library.
Citation:
Chi-Cheng Tsai, Ching-I Chung, Yi-Ting Huang, Chia-Hsing Shen, Yu-Chieh Wu, Jie-Chi Yang, "VCSR: Video Content Summarization for Recommendation," icalt, pp.862-864, Seventh IEEE International Conference on Advanced Learning Technologies (ICALT 2007), 2007
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